text:
interpretability. In particular, guaranteed monotonicity of the learned function with respect to some of the inputs can be critical for user confidence. We propose meeting these goals for low-dimensional machine learning problems by learning flexible, monotonic functions using calibrated interpolated look-up tables. We extend the structural risk minimization framework of lattice regression to monotonic functions by adding linear inequality constraints. In addition, we propose jointly learning interpretable calibrations of each feature to normalize continuous features and handle categorical or missing data, at the cost of making the objective non-convex. We address large-scale learning through parallelization, mini-batching, and random sampling of additive regularizer terms. Case studies on real-world problems with up to sixteen features and up to hundreds of millions of training samples demonstrate the proposed monotonic functions can achieve state-of-the-art accuracy in practice while providing greater transparency to users.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3790–3836},
numpages = {47},
keywords = {look-up tables, interpolation, monotonicity, interpretability}
}

@article{10.5555/2946645.3007061,
author = {Baktashmotlagh, Mahsa and Harandi, Mehrtash and Salzmann, Mathieu},
title = {Distribution-Matching Embedding for Visual Domain Adaptation},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Domain-invariant representations are key to addressing the domain shift problem where the training and test examples follow different distributions. Existing techniques that have attempted to match the distributions of the source and target domains typically compare these distributions in the original feature space. This space, however, may not be directly suitable for such a comparison, since some of the features may have been distorted by the domain shift, or may be domain specific. In this paper, we introduce a Distribution-Matching Embedding approach: An unsupervised domain adaptation method that overcomes this issue by mapping the data to a latent space where the distance between the empirical distributions of the source and target examples is minimized. In other words, we seek to extract the information that is invariant across the source and target data. In particular, we study two different distances to compare the source and target distributions: the Maximum Mean Discrepancy and the Hellinger distance. Furthermore, we show that our approach allows us to learn either a linear embedding, or a nonlinear one. We demonstrate the benefits of our approach on the tasks of visual object recognition, text categorization, and WiFi localization.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3760–3789},
numpages = {30},
keywords = {maximum mean discrepancy, hellinger distance, distribution matching, domain adaptation, domain invariant representations}
}

@article{10.5555/2946645.3007060,
author = {Shin, Hoo-Chang and Lu, Le and Kim, Lauren and Seff, Ari and Yao, Jianhua and Summers, Ronald M.},
title = {Interleaved Text/Image Deep Mining on a Large-Scale Radiology Database for Automated Image Interpretation},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Despite tremendous progress in computer vision, there has not been an attempt to apply machine learning on very large-scale medical image databases. We present an interleaved text/image deep learning system to extract and mine the semantic interactions of radiology images and reports from a national research hospital's Picture Archiving and Communication System. With natural language processing, we mine a collection of ∼216K representative two-dimensional images selected by clinicians for diagnostic reference and match the images with their descriptions in an automated manner. We then employ a weakly supervised approach using all of our available data to build models for generating approximate interpretations of patient images. Finally, we demonstrate a more strictly supervised approach to detect the presence and absence of a number of frequent disease types, providing more specific interpretations of patient scans. A relatively small amount of data is used for this part, due to the challenge in gathering quality labels from large raw text data. Our work shows the feasibility of large-scale learning and prediction in electronic patient records available in most modern clinical institutions. It also demonstrates the trade-offs to consider in designing machine learning systems for analyzing large medical data.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3729–3759},
numpages = {31},
keywords = {natural language processing, deep learning, medical Imaging, convolutional neural networks, topic models}
}

@article{10.5555/2946645.3007059,
author = {Yadwadkar, Neeraja J. and Hariharan, Bharath and Gonzalez, Joseph E. and Katz, Randy},
title = {Multi-Task Learning for Straggler Avoiding Predictive Job Scheduling},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Parallel processing frameworks (Dean and Ghemawat, 2004) accelerate jobs by breaking them into tasks that execute in parallel. However, slow running or straggler tasks can run up to 8 times slower than the median task on a production cluster (Ananthanarayanan et al., 2013), leading to delayed job completion and inefficient use of resources. Existing straggler mitigation techniques wait to detect stragglers and then relaunch them, delaying straggler detection and wasting resources. We built Wrangler (Yadwadkar et al., 2014), a system that predicts when stragglers are going to occur and makes scheduling decisions to avoid such situations. To capture node and workload variability, Wrangler built separate models for every node and workload, requiring the time-consuming collection of substantial training data. In this paper, we propose multi-task learning formulations that share information between the various models, allowing us to use less training data and bring training time down from 4 hours to 40 minutes. Unlike naive multi-task learning formulations, our formulations capture the shared structure in our data, improving generalization performance on limited data. Finally, we extend these formulations using group sparsity inducing norms to automatically discover the similarities between tasks and improve interpretability.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3692–3728},
numpages = {37}
}

@article{10.5555/2946645.3007058,
author = {Wang, Yu-Xiang and Sharpnack, James and Smola, Alexander J. and Tibshirani, Ryan J.},
title = {Trend Filtering on Graphs},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We introduce a family of adaptive estimators on graphs, based on penalizing the l1 norm of discrete graph differences. This generalizes the idea of trend filtering (Kim et al., 2009; Tibshirani, 2014), used for univariate nonparametric regression, to graphs. Analogous to the univariate case, graph trend filtering exhibits a level of local adaptivity unmatched by the usual l2-based graph smoothers. It is also defined by a convex minimization problem that is readily solved (e.g., by fast ADMM or Newton algorithms). We demonstrate the merits of graph trend filtering through both examples and theory.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3651–3691},
numpages = {41},
keywords = {graph smoothing, total variation denoising, local adaptivity, fused lasso, trend filtering}
}

@article{10.5555/2946645.3007057,
author = {Zuluaga, Marcela and Krause, Andreas and P\"{u}schel, Markus},
title = {ε-PAL: An Active Learning Approach to the Multi-Objective Optimization Problem},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {In many fields one encounters the challenge of identifying out of a pool of possible designs those that simultaneously optimize multiple objectives. In many applications an exhaustive search for the Pareto-optimal set is infeasible. To address this challenge, we propose the ε-Pareto Active Learning (ε-PAL) algorithm which adaptively samples the design space to predict a set of Pareto-optimal solutions that cover the true Pareto front of the design space with some granularity regulated by a parameter ε. Key features of ε-PAL include (1) modeling the objectives as draws from a Gaussian process distribution to capture structure and accommodate noisy evaluation; (2) a method to carefully choose the next design to evaluate to maximize progress; and (3) the ability to control prediction accuracy and sampling cost. We provide theoretical bounds on ε-PAL's sampling cost required to achieve a desired accuracy. Further, we perform an experimental evaluation on three real-world data sets that demonstrate ε-PAL's effectiveness; in comparison to the state-of-the-art active learning algorithm PAL, ε-PAL reduces the amount of computations and the number of samples from the design space required to meet the user's desired level of accuracy. In addition, we show that ε-PAL improves significantly over a state-of-the-art multi-objective optimization method, saving in most cases 30% to 70% evaluations to achieve the same accuracy.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3619–3650},
numpages = {32},
keywords = {active learning, pareto optimality, Bayesian optimization, multi-objective optimization, design space exploration}
}

@article{10.5555/2946645.3007056,
author = {Vehtari, Aki and Mononen, Tommi and Tolvanen, Ville and Sivula, Tuomas and Winther, Ole},
title = {Bayesian Leave-One-out Cross-Validation Approximations for Gaussian Latent Variable Models},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several Bayesian leave-one-out (LOO) cross-validation approximations that in most cases can be computed with a small additional cost after forming the posterior approximation given the full data. Our main objective is to assess the accuracy of the approximative LOO cross-validation estimators. That is, for each method (Laplace and EP) we compare the approximate fast computation with the exact brute force LOO computation. Secondarily, we evaluate the accuracy of the Laplace and EP approximations themselves against a ground truth established through extensive Markov chain Monte Carlo simulation. Our empirical results show that the approach based upon a Gaussian approximation to the LOO marginal distribution (the so-called cavity distribution) gives the most accurate and reliable results among the fast methods.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3581–3618},
numpages = {38},
keywords = {leave-one-out cross-validation, Laplace approximation, Gaussian latent variable model, expectation propagation, predictive performance}
}

@article{10.5555/2946645.3007055,
author = {Zhang, Yuchen and Chen, Xi and Zhou, Dengyong and Jordan, Michael I.},
title = {Spectral Methods Meet EM: A Provably Optimal Algorithm for Crowdsourcing},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Crowdsourcing is a popular paradigm for effectively collecting labels at low cost. The Dawid-Skene estimator has been widely used for inferring the true labels from the noisy labels provided by non-expert crowdsourcing workers. However, since the estimator maximizes a non-convex log-likelihood function, it is hard to theoretically justify its performance. In this paper, we propose a two-stage efficient algorithm for multi-class crowd labeling problems. The first stage uses the spectral method to obtain an initial estimate of parameters. Then the second stage refines the estimation by optimizing the objective function of the Dawid-Skene estimator via the EM algorithm. We show that our algorithm achieves the optimal convergence rate up to a logarithmic factor. We conduct extensive experiments on synthetic and real datasets. Experimental results demonstrate that the proposed algorithm is comparable to the most accurate empirical approach, while outperforming several other recently proposed methods.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3537–3580},
numpages = {44},
keywords = {EM, non-convex optimization, spectral methods, Dawid-Skene model, crowdsourcing, minimax rate}
}

@article{10.5555/2946645.3007054,
author = {He, Ru and Tian, Jin and Wu, Huaiqing},
title = {Structure Learning in Bayesian Networks of a Moderate Size by Efficient Sampling},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We study the Bayesian model averaging approach to learning Bayesian network structures (DAGs) from data. We develop new algorithms including the first algorithm that is able to efficiently sample DAGs of a moderate size (with up to about 25 variables) according to the exact structure posterior. The DAG samples can then be used to construct estimators for the posterior of any feature. We theoretically prove good properties of our estimators and empirically show that our estimators considerably outperform the estimators from the previous state-of-the-art methods.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3483–3536},
numpages = {54},
keywords = {DAG sampling, dynamic programming, structure learning, order sampling, Bayesian model averaging, Bayesian networks}
}

@article{10.5555/2946645.3007053,
author = {Guo, Zijian and Small, Dylan S.},
title = {Control Function Instrumental Variable Estimation of Nonlinear Causal Effect Models},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The instrumental variable method consistently estimates the effect of a treatment when there is unmeasured confounding and a valid instrumental variable. A valid instrumental variable is a variable that is independent of unmeasured confounders and affects the treatment but does not have a direct effect on the outcome beyond its effect on the treatment. Two commonly used estimators for using an instrumental variable to estimate a treatment effect are the two stage least squares estimator and the control function estimator. For linear causal effect models, these two estimators are equivalent, but for nonlinear causal effect models, the estimators are different. We provide a systematic comparison of these two estimators for nonlinear causal effect models and develop an approach to combing the two estimators that generally performs better than either one alone. We show that the control function estimator is a two stage least squares estimator with an augmented set of instrumental variables. If these augmented instrumental variables are valid, then the control function estimator can be much more efficient than usual two stage least squares without the augmented instrumental variables while if the augmented instrumental variables are not valid, then the control function estimator may be inconsistent while the usual two stage least squares remains consistent. We apply the Hausman test to test whether the augmented instrumental variables are valid and construct a pretest estimator based on this test. The pretest estimator is shown to work well in a simulation study. An application to the effect of exposure to violence on time preference is considered.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3448–3482},
numpages = {35},
keywords = {endogenous variable, control function estimator, two stage least squares estimator, instrumental variable method, causal inference, pretest estimator}
}

@article{10.5555/2946645.3007052,
author = {Melchior, Jan and Fischer, Asja and Wiskott, Laurenz},
title = {How to Center Deep Boltzmann Machines},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {This work analyzes centered Restricted Boltzmann Machines (RBMs) and centered Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centered and normal Boltzmann Machines (BMs) and thus RBMs and DBMs are different parameterizations of the same model class, such that any normal BM/RBM/DBM can be transformed to an equivalent centered BM/RBM/DBM and vice versa, and that this equivalence generalizes to artificial neural networks in general, (ii) the expected performance of centered binary BMs/RBMs/DBMs is invariant under simultaneous flip of data and offsets, for any off-set value in the range of zero to one, (iii) centering can be reformulated as a different update rule for normal BMs/RBMs/DBMs, and (iv) using the enhanced gradient is equivalent to setting the offset values to the average over model and data mean. Furthermore, we present numerical simulations suggesting that (i) optimal generative performance is achieved by subtracting mean values from visible as well as hidden variables, (ii) centered binary RBMs/DBMs reach significantly higher log-likelihood values than normal binary RBMs/DBMs, (iii) centering variants whose offsets depend on the model mean, like the enhanced gradient, suffer from severe divergence problems, (iv) learning is stabilized if an exponentially moving average over the batch means is used for the offset values instead of the current batch mean, which also prevents the enhanced gradient from severe divergence, (v) on a similar level of log-likelihood values centered binary RBMs/DBMs have smaller weights and bigger bias parameters than normal binary RBMs/DBMs, (vi) centering leads to an update direction that is closer to the natural gradient, which is extremely efficient for training as we show for small binary RBMs, (vii) centering eliminates the need for greedy layer-wise pre-training of DBMs, which often even deteriorates the results independently of whether centering is used or not, and (ix) centering is also beneficial for auto encoders.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3387–3447},
numpages = {61},
keywords = {artificial neural network, stochastic maximum likelihood, generative model, contrastive divergence, parallel tempering, deep Boltzmann machine, auto encoder, restricted Boltzmann machine, natural gradient, enhanced gradient, centering}
}

@article{10.5555/2946645.3007051,
author = {Babbar, Rohit and Partalas, Ioannis and Gaussier, Eric and Amini, Massih-Reza and Amblard, C\'{e}cile},
title = {Learning Taxonomy Adaptation in Large-Scale Classification},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {In this paper, we study flat and hierarchical classification strategies in the context of large-scale taxonomies. Addressing the problem from a learning-theoretic point of view, we first propose a multi-class, hierarchical data dependent bound on the generalization error of classifiers deployed in large-scale taxonomies. This bound provides an explanation to several empirical results reported in the literature, related to the performance of flat and hierarchical classifiers. Based on this bound, we also propose a technique for modifying a given taxonomy through pruning, that leads to a lower value of the upper bound as compared to the original taxonomy. We then present another method for hierarchy pruning by studying approximation error of a family of classifiers, and derive from it features used in a meta-classifier to decide which nodes to prune. We finally illustrate the theoretical developments through several experiments conducted on two widely used taxonomies.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3350–3386},
numpages = {37},
keywords = {hierarchical classification, large-scale classification, rademacher complexity, taxonomy adaptation, meta-learning}
}

@article{10.5555/2946645.3007050,
author = {Leifert, Gundram and Strau\ss{}, Tobias and Gr\"{u}ning, Tobias and Wustlich, Welf and Labahn, Roger},
title = {Cells in Multidimensional Recurrent Neural Networks},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The transcription of handwritten text on images is one task in machine learning and one solution to solve it is using multi-dimensional recurrent neural networks (MDRNN) with connectionist temporal classification (CTC). The RNNs can contain special units, the long short-term memory (LSTM) cells. They are able to learn long term dependencies but they get unstable when the dimension is chosen greater than one. We defined some useful and necessary properties for the one-dimensional LSTM cell and extend them in the multi-dimensional case. Thereby we introduce several new cells with better stability. We present a method to design cells using the theory of linear shift invariant systems. The new cells are compared to the LSTM cell on the IFN/ENIT and Rimes database, where we can improve the recognition rate compared to the LSTM cell. So each application where the LSTM cells in MDRNNs are used could be improved by substituting them by the new developed cells.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3313–3349},
numpages = {37},
keywords = {LSTM, MDRNN, handwriting recognition, CTC, neural network}
}

@article{10.5555/2946645.3007049,
author = {Juba, Brendan},
title = {Integrated Common Sense Learning and Planning in POMDPs},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We formulate a new variant of the problem of planning in an unknown environment, for which we can provide algorithms with reasonable theoretical guarantees in spite of large state spaces and time horizons, partial observability, and complex dynamics. In this variant, an agent is given a collection of example traces produced by a reference policy, which may, for example, capture the agent's past behavior. The agent is (only) asked to find policies that are supported by regularities in the dynamics that are observable on these example traces. We describe an efficient algorithm that uses such "common sense" knowledge reflected in the example traces to construct decision tree policies for goal-oriented factored POMDPs. More precisely, our algorithm (provably) succeeds at finding policy for a given input goal when (1) there is a CNF that is almost always observed satisfied on the traces of the POMDP, capturing a sufficient approximation of its dynamics and (2) for a decision tree policy of bounded complexity, there exist small-space resolution proofs that the goal is achieved on each branch using the aforementioned CNF capturing the "common sense rules." Such a CNF always exists for noisy STRIPS domains, for example. Our results thus essentially establish that the possession of a suitable exploration policy for collecting the necessary examples is the fundamental obstacle to learning to act in such environments.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3276–3312},
numpages = {37},
keywords = {partially observed Markov decision process, decision tree policies, non-monontonic reasoning, PAC-semantics, noisy STRIPS}
}

@article{10.5555/2946645.3007048,
author = {Reyes, Oscar and P\'{e}rez, Eduardo and Del Carmen Rodr\'{\i}guez-Hern\'{a}ndez, Mar\'{\i}a and Fardoun, Habib M. and Ventura, Sebasti\'{a}n},
title = {JCLAL: A Java Framework for Active Learning},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Active Learning has become an important area of research owing to the increasing number of real-world problems which contain labelled and unlabelled examples at the same time. JCLAL is a Java Class Library for Active Learning which has an architecture that follows strong principles of object-oriented design. It is easy to use, and it allows the developers to adapt, modify and extend the framework according to their needs. The library offers a variety of active learning methods that have been proposed in the literature. The software is available under the GPL license.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3271–3275},
numpages = {5},
keywords = {object-oriented design, framework, java language, active learning}
}

@article{10.5555/2946645.3007047,
author = {Petersen, Ashley and Simon, Noah and Witten, Daniela},
title = {Convex Regression with Interpretable Sharp Partitions},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3240–3270},
numpages = {31},
keywords = {non-additivity, non-parametric regression, convex optimization, prediction, interpretability}
}

@article{10.5555/2946645.3007046,
author = {Daniel, Christian and Neumann, Gerhard and Kroemer, Oliver and Peters, Jan},
title = {Hierarchical Relative Entropy Policy Search},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Many reinforcement learning (RL) tasks, especially in robotics, consist of multiple sub-tasks that are strongly structured. Such task structures can be exploited by incorporating hierarchical policies that consist of gating networks and sub-policies. However, this concept has only been partially explored for real world settings and complete methods, derived from first principles, are needed. Real world settings are challenging due to large and continuous state-action spaces that are prohibitive for exhaustive sampling methods. We define the problem of learning sub-policies in continuous state action spaces as finding a hierarchical policy that is composed of a high-level gating policy to select the low-level sub-policies for execution by the agent. In order to efficiently share experience with all sub-policies, also called inter-policy learning, we treat these sub-policies as latent variables which allows for distribution of the update information between the sub-policies. We present three different variants of our algorithm, designed to be suitable for a wide variety of real world robot learning tasks and evaluate our algorithms in two real robot learning scenarios as well as several simulations and comparisons.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3190–3239},
numpages = {50},
keywords = {temporal correlation, reinforcement learning, structured learning, hierarchical learning, policy search, REPS, motor skill learning, HiREPS, robust learning, robot learning}
}

@article{10.5555/2946645.3007045,
author = {Yang, Dan and Ma, Zongming and Buja, Andreas},
title = {Rate Optimal Denoising of Simultaneously Sparse and Low Rank Matrices},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We study minimax rates for denoising simultaneously sparse and low rank matrices in high dimensions. We show that an iterative thresholding algorithm achieves (near) optimal rates adaptively under mild conditions for a large class of loss functions. Numerical experiments on synthetic datasets also demonstrate the competitive performance of the proposed method.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3163–3189},
numpages = {27},
keywords = {high dimensionality, denoising, minimax rates, sparse SVD, low rank matrices, simultaneously structured matrices, sparsity}
}

@article{10.5555/2946645.3007044,
author = {Kumar, M. Pawan and Dokania, Puneet K.},
title = {Rounding-Based Moves for Semi-Metric Labeling},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Semi-metric labeling is a special case of energy minimization for pairwise Markov random fields. The energy function consists of arbitrary unary potentials, and pairwise potentials that are proportional to a given semi-metric distance function over the label set. Popular methods for solving semi-metric labeling include (i) move-making algorithms, which iteratively solve a minimum st-cut problem; and (ii) the linear programming (LP) relaxation based approach. In order to convert the fractional solution of the LP relaxation to an integer solution, several randomized rounding procedures have been developed in the literature. We consider a large class of parallel rounding procedures, and design move-making algorithms that closely mimic them. We prove that the multiplicative bound of a move-making algorithm exactly matches the approximation factor of the corresponding rounding procedure for any arbitrary distance function. Our analysis includes all known results for move-making algorithms as special cases.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3121–3162},
numpages = {42},
keywords = {move-making algorithms, linear programming relaxation, semi-metric labeling, multiplicative bounds}
}

@article{10.5555/2946645.3007043,
author = {Gomez-Rodriguez, Manuel and Song, Le and Daneshmand, Hadi and Sch\"{o}lkopf, Bernhard},
title = {Estimating Diffusion Networks: Recovery Conditions, Sample Complexity & Soft-Thresholding Algorithm},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Information spreads across social and technological networks, but often the network structures are hidden from us and we only observe the traces left by the diffusion processes, called cascades. Can we recover the hidden network structures from these observed cascades? What kind of cascades and how many cascades do we need? Are there some network structures which are more difficult than others to recover? Can we design efficient inference algorithms with provable guarantees?Despite the increasing availability of cascade data and methods for inferring networks from these data, a thorough theoretical understanding of the above questions remains largely unexplored in the literature. In this paper, we investigate the network structure inference problem for a general family of continuous-time diffusion models using an l1- regularized likelihood maximization framework. We show that, as long as the cascade sampling process satisfies a natural incoherence condition, our framework can recover the correct network structure with high probability if we observe O(d3logN) cascades, where d is the maximum number of parents of a node and N is the total number of nodes. Moreover, we develop a simple and efficient soft-thresholding network inference algorithm which demonstrate the match between our theoretical prediction and empirical results. In practice, this new algorithm also outperforms other alternatives in terms of the accuracy of recovering hidden diffusion networks.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3092–3120},
numpages = {29}
}

@article{10.5555/2946645.3007042,
author = {Guo, Xin and Fan, Jun and Zhou, Ding-Xuan},
title = {Sparsity and Error Analysis of Empirical Feature-Based Regularization Schemes},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We consider a learning algorithm generated by a regularization scheme with a concave regularizer for the purpose of achieving sparsity and good learning rates in a least squares regression setting. The regularization is induced for linear combinations of empirical features, constructed in the literatures of kernel principal component analysis and kernel projection machines, based on kernels and samples. In addition to the separability of the involved optimization problem caused by the empirical features, we carry out sparsity and error analysis, giving bounds in the norm of the reproducing kernel Hilbert space, based on a priori conditions which do not require assumptions on sparsity in terms of any basis or system. In particular, we show that as the concave exponent q of the concave regularizer increases to 1, the learning ability of the algorithm improves. Some numerical simulations for both artificial and real MHC-peptide binding data involving the lq regularizer and the SCAD penalty are presented to demonstrate the sparsity and error analysis.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3058–3091},
numpages = {34},
keywords = {SCAD penalty, regularization with empirical features, concave regularizer, reproducing kernel Hilbert space, sparsity, lq-penalty}
}

@article{10.5555/2946645.3007041,
author = {Fogel, Fajwel and d'Aspremont, Alexandre and Vojnovic, Milan},
title = {Spectral Ranking Using Seriation},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder this matrix and construct a ranking. We first show that this spectral seriation algorithm recovers the true ranking when all pairwise comparisons are observed and consistent with a total order. We then show that ranking reconstruction is still exact when some pairwise comparisons are corrupted or missing, and that seriation based spectral ranking is more robust to noise than classical scoring methods. Finally, we bound the ranking error when only a random subset of the comparions are observed. An additional benefit of the seriation formulation is that it allows us to solve semi-supervised ranking problems. Experiments on both synthetic and real datasets demonstrate that seriation based spectral ranking achieves competitive and in some cases superior performance compared to classical ranking methods.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {3013–3057},
numpages = {45},
keywords = {ranking, seriation, spectral methods}
}

@article{10.5555/2946645.3007040,
author = {Neykov, Matey and Liu, Jun S. and Cai, Tianxi},
title = {L<sub>1</sub>-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {It is known that for a certain class of single index models (SIMs) Y = f(Xp\texttimes{}1T β0, ε), support recovery is impossible when X ∼ N(0; Ip\texttimes{}p) and a model complexity adjusted sample size is below a critical threshold. Recently, optimal algorithms based on Sliced Inverse Regression (SIR) were suggested. These algorithms work provably under the assumption that the design X comes from an i.i.d. Gaussian distribution. In the present paper we analyze algorithms based on covariance screening and least squares with L1 penalization (i.e. LASSO) and demonstrate that they can also enjoy optimal (up to a scalar) rescaled sample size in terms of support recovery, albeit under slightly different assumptions on f and ε compared to the SIR based algorithms. Furthermore, we show more generally, that LASSO succeeds in recovering the signed support of β0 if X ∼ N(0, Σ), and the covariance Σ satisfies the irrepresentable condition. Our work extends existing results on the support recovery of LASSO for the linear model, to a more general class of SIMs.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2976–3012},
numpages = {37},
keywords = {single index models, high-dimensional statistics, support recovery, LASSO, sparsity}
}

@article{10.5555/2946645.3007039,
author = {Chin, Wei-Sheng and Yuan, Bo-Wen and Yang, Meng-Yuan and Zhuang, Yong and Juan, Yu-Chin and Lin, Chih-Jen},
title = {LIBMF: A Library for Parallel Matrix Factorization in Shared-Memory Systems},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Matrix factorization (MF) plays a key role in many applications such as recommender systems and computer vision, but MF may take long running time for handling large matrices commonly seen in the big data era. Many parallel techniques have been proposed to reduce the running time, but few parallel MF packages are available. Therefore, we present an open source library, LIBMF, based on recent advances of parallel MF for shared-memory systems. LIBMF includes easy-to-use command-line tools, interfaces to C/C++ languages, and comprehensive documentation. Our experiments demonstrate that LIBMF outperforms state of the art packages. LIBMF is BSD-licensed, so users can freely use, modify, and redistribute the code.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2971–2975},
numpages = {5},
keywords = {matrix factorization, one-class matrix factorization, logistic matrix factorization, binary matrix factorization, adaptive learning rate, stochastic gradient method, parallel computation, non-negative matrix factorization}
}

@article{10.5555/2946645.3007038,
author = {Fan, Jun and Wu, Yirong and Yuan, Ming and Page, David and Liu, Jie and Ong, Irene M. and Peissig, Peggy and Burnside, Elizabeth},
title = {Structure-Leveraged Methods in Breast Cancer Risk Prediction},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Predicting breast cancer risk has long been a goal of medical research in the pursuit of precision medicine. The goal of this study is to develop novel penalized methods to improve breast cancer risk prediction by leveraging structure information in electronic health records. We conducted a retrospective case-control study, garnering 49 mammography descriptors and 77 high-frequency/low-penetrance single-nucleotide polymorphisms (SNPs) from an existing personalized medicine data repository. Structured mammography reports and breast imaging features have long been part of a standard electronic health record (EHR), and genetic markers likely will be in the near future. Lasso and its variants are widely used approaches to integrated learning and feature selection, and our methodological contribution is to incorporate the dependence structure among the features into these approaches. More specifically, we propose a new methodology by combining group penalty and lp (1 ≤ p ≤ 2) fusion penalty to improve breast cancer risk prediction, taking into account structure information in mammography descriptors and SNPs. We demonstrate that our method provides benefits that are both statistically significant and potentially significant to people's lives.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2956–2970},
numpages = {15},
keywords = {mammography descriptors, structure information, genetic variants, personalized medicine, breast cancer risk prediction}
}

@article{10.5555/2946645.3007037,
author = {Wei, Ermo and Luke, Sean},
title = {Lenient Learning in Independent-Learner Stochastic Cooperative Games},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We introduce the Lenient Multiagent Reinforcement Learning 2 (LMRL2) algorithm for independent-learner stochastic cooperative games. LMRL2 is designed to overcome a pathology called relative overgeneralization, and to do so while still performing well in games with stochastic transitions, stochastic rewards, and miscoordination. We discuss the existing literature, then compare LMRL2 against other algorithms drawn from the literature which can be used for games of this kind: traditional ("Distributed") Q-learning, Hysteretic Q-learning, WoLF-PHC, SOoN, and (for repeated games only) FMQ. The results show that LMRL2 is very effective in both of our measures (complete and correct policies), and is found in the top rank more often than any other technique. LMRL2 is also easy to tune: though it has many available parameters, almost all of them stay at default settings. Generally the algorithm is optimally tuned with a single parameter, if any. We then examine and discuss a number of side-issues and options for LMRL2.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2914–2955},
numpages = {42},
keywords = {lenient learning, independent learner, multiagent learning, game theory, reinforcement learning}
}

@article{10.5555/2946645.3007036,
author = {Diamond, Steven and Boyd, Stephen},
title = {CVXPY: A Python-Embedded Modeling Language for Convex Optimization},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2909–2913},
numpages = {5},
keywords = {domain-specific languages, convexity verification, convex optimization, conic programming, Python}
}

@article{10.5555/2946645.3007035,
author = {Yang, Lei and Lv, Shaogao and Wang, Junhui},
title = {Model-Free Variable Selection in Reproducing Kernel Hilbert Space},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Variable selection is popular in high-dimensional data analysis to identify the truly informative variables. Many variable selection methods have been developed under various model assumptions. Whereas success has been widely reported in literature, their performances largely depend on validity of the assumed models, such as the linear or additive models. This article introduces a model-free variable selection method via learning the gradient functions. The idea is based on the equivalence between whether a variable is informative and whether its corresponding gradient function is substantially non-zero. The proposed variable selection method is then formulated in a framework of learning gradients in a flexible reproducing kernel Hilbert space. The key advantage of the proposed method is that it requires no explicit model assumption and allows for general variable effects. Its asymptotic estimation and selection consistencies are studied, which establish the convergence rate of the estimated sparse gradients and assure that the truly informative variables are correctly identified in probability. The effectiveness of the proposed method is also supported by a variety of simulated examples and two real-life examples.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2885–2908},
numpages = {24},
keywords = {high-dimensional data, learning gradients, group Lasso, kernel regression, variable selection, reproducing kernel Hilbert space (RKHS)}
}

@article{10.5555/2946645.3007034,
author = {Maurer, Andreas and Pontil, Massimiliano and Romera-Paredes, Bernardino},
title = {The Benefit of Multitask Representation Learning},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We discuss a general method to learn data representations from multiple tasks. We provide a justification for this method in both settings of multitask learning and learning-to-learn. The method is illustrated in detail in the special case of linear feature learning. Conditions on the theoretical advantage offered by multitask representation learning over independent task learning are established. In particular, focusing on the important example of half-space learning, we derive the regime in which multitask representation learning is beneficial over independent task learning, as a function of the sample size, the number of tasks and the intrinsic data dimensionality. Other potential applications of our results include multitask feature learning in reproducing kernel Hilbert spaces and multilayer, deep networks.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2853–2884},
numpages = {32},
keywords = {statistical learning theory, learning-to-learn, multitask learning, representation learning, transfer learning}
}

@article{10.5555/2946645.3007033,
author = {Wang, Xin and Bi, Jinbo and Yu, Shipeng and Sun, Jiangwen and Song, Minghu},
title = {Multiplicative Multitask Feature Learning},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We investigate a general framework of multiplicative multitask feature learning which decomposes individual task's model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers. Further, an analytical formula is derived for the across-task component as related to the task-specific component for all these regularizers, leading to a better understanding of the shrinkage effects of different regularizers. Study of this framework motivates new multitask learning algorithms. We propose two new learning formulations by varying the parameters in the proposed framework. An effcient blockwise coordinate descent algorithm is developed suitable for solving the entire family of formulations with rigorous convergence analysis. Simulation studies have identified the statistical properties of data that would be in favor of the new formulations. Extensive empirical studies on various classification and regression benchmark data sets have revealed the relative advantages of the two new formulations by comparing with the state of the art, which provides instructive insights into the feature learning problem with multiple tasks.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2820–2852},
numpages = {33},
keywords = {blockwise coordinate descent, regularization, sparse modeling, multitask learning}
}

@article{10.5555/2946645.3007032,
author = {Wiens, Jenna and Guttag, John and Horvitz, Eric},
title = {Patient Risk Stratification with Time-Varying Parameters: A Multitask Learning Approach},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The proliferation of electronic health records (EHRs) frames opportunities for using machine learning to build models that help healthcare providers improve patient outcomes. However, building useful risk stratification models presents many technical challenges including the large number of factors (both intrinsic and extrinsic) influencing a patient's risk of an adverse outcome and the inherent evolution of that risk over time. We address these challenges in the context of learning a risk stratification model for predicting which patients are at risk of acquiring a Clostridium difficile infection (CDI). We take a novel data-centric approach, leveraging the contents of EHRs from nearly 50,000 hospital admissions. We show how, by adapting techniques from multitask learning, we can learn models for patient risk stratification with unprecedented classification performance. Our model, based on thousands of variables, both time-varying and time-invariant, changes over the course of a patient admission. Applied to a held out set of approximately 25,000 patient admissions, we achieve an area under the receiver operating characteristic curve of 0.81 (95% CI 0.78-0.84). The model has been integrated into the health record system at a large hospital in the US, and can be used to produce daily risk estimates for each inpatient. While more complex than traditional risk stratification methods, the widespread development and use of such data-driven models could ultimately enable cost-effective, targeted prevention strategies that lead to better patient outcomes.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2797–2819},
numpages = {23},
keywords = {healthcare-associated infections, risk stratification, Clostridium difficile, multitask learning, time-varying coefficients}
}

@article{10.5555/2946645.3007031,
author = {Ho, Qirong and Yin, Junming and Xing, Eric P.},
title = {Latent Space Inference of Internet-Scale Networks},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The rise of Internet-scale networks, such as web graphs and social media with hundreds of millions to billions of nodes, presents new scientific opportunities, such as overlapping community detection to discover the structure of the Internet, or to analyze trends in online social behavior. However, many existing probabilistic network models are difficult or impossible to deploy at these massive scales. We propose a scalable approach for modeling and inferring latent spaces in Internet-scale networks, with an eye towards overlapping community detection as a key application. By applying a succinct representation of networks as a bag of triangular motifs, developing a parsimonious statistical model, deriving an efficient stochastic variational inference algorithm, and implementing it as a distributed cluster program via the Petuum parameter server system, we demonstrate overlapping community detection on real networks with up to 100 million nodes and 1000 communities on 5 machines in under 40 hours. Compared to other state-of-the-art probabilistic network approaches, our method is several orders of magnitude faster, with competitive or improved accuracy at overlapping community detection.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2756–2796},
numpages = {41},
keywords = {big data, probabilistic network models, distributed computation, triangular modeling, stochastic variational inference}
}

@article{10.5555/2946645.3007030,
author = {Lin, Junhong and Rosasco, Lorenzo and Zhou, Ding-Xuan},
title = {Iterative Regularization for Learning with Convex Loss Functions},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We consider the problem of supervised learning with convex loss functions and propose a new form of iterative regularization based on the subgradient method. Unlike other regularization approaches, in iterative regularization no constraint or penalization is considered, and generalization is achieved by (early) stopping an empirical iteration. We consider a nonparametric setting, in the framework of reproducing kernel Hilbert spaces, and prove consistency and finite sample bounds on the excess risk under general regularity conditions. Our study provides a new class of efficient regularized learning algorithms and gives insights on the interplay between statistics and optimization in machine learning.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2718–2755},
numpages = {38}
}

@article{10.5555/2946645.3007029,
author = {Cl\'{e}men\c{c}on, Stephan and Colin, Igor and Bellet, Aur\'{e}lien},
title = {Scaling-up Empirical Risk Minimization: Optimization of Incomplete U-Statistics},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {In a wide range of statistical learning problems such as ranking, clustering or metric learning among others, the risk is accurately estimated by U-statistics of degree d ≥ 1, i.e. functionals of the training data with low variance that take the form of averages over k-tuples. From a computational perspective, the calculation of such statistics is highly expensive even for a moderate sample size n, as it requires averaging O(nd) terms. This makes learning procedures relying on the optimization of such data functionals hardly feasible in practice. It is the major goal of this paper to show that, strikingly, such empirical risks can be replaced by drastically computationally simpler Monte-Carlo estimates based on O(n) terms only, usually referred to as incomplete U-statistics, without damaging the OP(1/√n) learning rate of Empirical Risk Minimization (ERM) procedures. For this purpose, we establish uniform deviation results describing the error made when approximating a U-process by its incomplete version under appropriate complexity assumptions. Extensions to model selection, fast rate situations and various sampling techniques are also considered, as well as an application to stochastic gradient descent for ERM. Finally, numerical examples are displayed in order to provide strong empirical evidence that the approach we promote largely surpasses more naive subsampling techniques.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2682–2717},
numpages = {36},
keywords = {big data, U-processes, stochastic gradient descent, rate bound analysis, empirical risk minimization, sampling design}
}

@article{10.5555/2946645.3007028,
author = {Richt\'{a}rik, Peter and Tak\'{a}\v{c}, Martin},
title = {Distributed Coordinate Descent Method for Learning with Big Data},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {In this paper we develop and analyze Hydra: HYbriD cooRdinAte descent method for solving loss minimization problems with big data. We initially partition the coordinates (features) and assign each partition to a different node of a cluster. At every iteration, each node picks a random subset of the coordinates from those it owns, independently from the other computers, and in parallel computes and applies updates to the selected coordinates based on a simple closed-form formula. We give bounds on the number of iterations sufficient to approximately solve the problem with high probability, and show how it depends on the data and on the partitioning. We perform numerical experiments with a LASSO instance described by a 3TB matrix.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2657–2681},
numpages = {25},
keywords = {distributed algorithms, stochastic methods, boosting, parallel coordinate descent}
}

@article{10.5555/2946645.3007027,
author = {Mohri, Mehryar and Medina, Andr\'{e}s Mu\~{n}oz},
title = {Learning Algorithms for Second-Price Auctions with Reserve},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Second-price auctions with reserve play a critical role in the revenue of modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function. We further give novel algorithms for solving this problem and report the results of several experiments in both synthetic and real-world data demonstrating their effectiveness.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2632–2656},
numpages = {25},
keywords = {revenue optimization, learning theory, auctions}
}

@article{10.5555/2946645.3007026,
author = {Sutton, Richard S. and Mahmood, A. Rupam and White, Martha},
title = {An Emphatic Approach to the Problem of Off-Policy Temporal-Difference Learning},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {In this paper we introduce the idea of improving the performance of parametric temporal-difference (TD) learning algorithms by selectively emphasizing or de-emphasizing their updates on different time steps. In particular, we show that varying the emphasis of linear TD(γ)'s updates in a particular way causes its expected update to become stable under off-policy training. The only prior model-free TD methods to achieve this with per-step computation linear in the number of function approximation parameters are the gradient-TD family of methods including TDC, GTD(γ), and GQ(λ). Compared to these methods, our emphatic TD(λ) is simpler and easier to use; it has only one learned parameter vector and one step-size parameter. Our treatment includes general state-dependent discounting and bootstrapping functions, and a way of specifying varying degrees of interest in accurately valuing different states.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2603–2631},
numpages = {29},
keywords = {temporal-difference learning, convergence, function approximation, stability, off-policy learning}
}

@article{10.5555/2946645.3007025,
author = {Escalera, Sergio and Athitsos, Vassilis and Guyon, Isabelle},
title = {Challenges in Multimodal Gesture Recognition},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {This paper surveys the state of the art on multimodal gesture recognition and introduces the JMLR special topic on gesture recognition 2011-2015. We began right at the start of the Kinect™ revolution when inexpensive infrared cameras providing image depth recordings became available. We published papers using this technology and other more conventional methods, including regular video cameras, to record data, thus providing a good overview of uses of machine learning and computer vision using multimodal data in this area of application. Notably, we organized a series of challenges and made available several datasets we recorded for that purpose, including tens of thousands of videos, which are available to conduct further research. We also overview recent state of the art works on gesture recognition based on a proposed taxonomy for gesture recognition, discussing challenges and future lines of research.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2549–2602},
numpages = {54},
keywords = {Kinect™, wearable sensors, time series analysis, pattern recognition, infrared cameras, multimodal data analysis, gesture recognition, computer vision}
}

@article{10.5555/2946645.3007024,
author = {Giscard, P.-L. and Choo, Z. and Thwaite, S. J. and Jaksch, D.},
title = {Exact Inference on Gaussian Graphical Models of Arbitrary Topology Using Path-Sums},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We present the path-sum formulation for exact statistical inference of marginals on Gaussian graphical models of arbitrary topology. The path-sum formulation gives the covariance between each pair of variables as a branched continued fraction of finite depth and breadth. Our method originates from the closed-form resummation of infinite families of terms of the walk-sum representation of the covariance matrix. We prove that the path-sum formulation always exists for models whose covariance matrix is positive definite: i.e. it is valid for both walk-summable and non-walk-summable graphical models of arbitrary topology. We show that for graphical models on trees the path-sum formulation is equivalent to Gaussian belief propagation. We also recover, as a corollary, an existing result that uses determinants to calculate the covariance matrix. We show that the path-sum formulation formulation is valid for arbitrary partitions of the inverse covariance matrix. We give detailed examples demonstrating our results.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2530–2548},
numpages = {19},
keywords = {belief propagation, path-sum, walk-sum, block matrices, graphs of arbitrary topology, Gaussian graphical models}
}

@article{10.5555/2946645.3007023,
author = {Neykov, Matey and Liu, Jun S. and Cai, Tianxi},
title = {On the Characterization of a Class of Fisher-Consistent Loss Functions and Its Application to Boosting},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Accurate classification of categorical outcomes is essential in a wide range of applications. Due to computational issues with minimizing the empirical 0/1 loss, Fisher consistent losses have been proposed as viable proxies. However, even with smooth losses, direct minimization remains a daunting task. To approximate such a minimizer, various boosting algorithms have been suggested. For example, with exponential loss, the AdaBoost algorithm (Freund and Schapire, 1995) is widely used for two-class problems and has been extended to the multi-class setting (Zhu et al., 2009). Alternative loss functions, such as the logistic and the hinge losses, and their corresponding boosting algorithms have also been proposed (Zou et al., 2008; Wang, 2012). In this paper we demonstrate that a broad class of losses, including non-convex functions, achieve Fisher consistency, and in addition can be used for explicit estimation of the conditional class probabilities. Furthermore, we provide a generic boosting algorithm that is not loss-specific. Extensive simulation results suggest that the proposed boosting algorithms could outperform existing methods with properly chosen losses and bags of weak learners.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2498–2529},
numpages = {32},
keywords = {SAMME, fisher-consistency, boosting, multiclass classification}
}

@article{10.5555/2946645.3007022,
author = {Guhaniyogi, Rajarshi and Dunson, David B.},
title = {Compressed Gaussian Process for Manifold Regression},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Nonparametric regression for large numbers of features (p) is an increasingly important problem. If the sample size n is massive, a common strategy is to partition the feature space, and then separately apply simple models to each partition set. This is not ideal when n is modest relative to p, and we propose an alternative approach relying on random compression of the feature vector combined with Gaussian process regression. The proposed approach is particularly motivated by the setting in which the response is conditionally independent of the features given the projection to a low dimensional manifold. Conditionally on the random compression matrix and a smoothness parameter, the posterior distribution for the regression surface and posterior predictive distributions are available analytically. Running the analysis in parallel for many random compression matrices and smoothness parameters, model averaging is used to combine the results. The algorithm can be implemented rapidly even in very large p and moderately large n nonparametric regression, has strong theoretical justification, and is found to yield state of the art predictive performance.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2472–2497},
numpages = {26},
keywords = {manifold regression, Gaussian random projection, Gaussian process, large p, compressed regression}
}

@article{10.5555/2946645.3007021,
author = {Russo, Daniel and Van Roy, Benjamin},
title = {An Information-Theoretic Analysis of Thompson Sampling},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We provide an information-theoretic analysis of Thompson sampling that applies across a broad range of online optimization problems in which a decision-maker must learn from partial feedback. This analysis inherits the simplicity and elegance of information theory and leads to regret bounds that scale with the entropy of the optimal-action distribution. This strengthens preexisting results and yields new insight into how information improves performance.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2442–2471},
numpages = {30},
keywords = {mutli-armed bandit, regret bounds, online optimization, Thompson sampling, information theory}
}

@article{10.5555/2946645.3007020,
author = {Barreto, Andr\'{e} M. S. and Precup, Doina and Pineau, Joelle},
title = {Practical Kernel-Based Reinforcement Learning},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Kernel-based reinforcement learning (KBRL) stands out among approximate reinforcement learning algorithms for its strong theoretical guarantees. By casting the learning problem as a local kernel approximation, KBRL provides a way of computing a decision policy which converges to a unique solution and is statistically consistent. Unfortunately, the model constructed by KBRL grows with the number of sample transitions, resulting in a computational cost that precludes its application to large-scale or on-line domains. In this paper we introduce an algorithm that turns KBRL into a practical reinforcement learning tool. Kernel-based stochastic factorization (KBSF) builds on a simple idea: when a transition probability matrix is represented as the product of two stochastic matrices, one can swap the factors of the multiplication to obtain another transition matrix, potentially much smaller than the original, which retains some fundamental properties of its precursor. KBSF exploits such an insight to compress the information contained in KBRL's model into an approximator of fixed size. This makes it possible to build an approximation considering both the difficulty of the problem and the associated computational cost. KBSF's computational complexity is linear in the number of sample transitions, which is the best one can do without discarding data. Moreover, the algorithm's simple mechanics allow for a fully incremental implementation that makes the amount of memory used independent of the number of sample transitions. The result is a kernel-based reinforcement learning algorithm that can be applied to large-scale problems in both off-line and on-line regimes. We derive upper bounds for the distance between the value functions computed by KBRL and KBSF using the same data. We also prove that it is possible to control the magnitude of the variables appearing in our bounds, which means that, given enough computational resources, we can make KBSF's value function as close as desired to the value function that would be computed by KBRL using the same set of sample transitions. The potential of our algorithm is demonstrated in an extensive empirical study in which KBSF is applied to difficult tasks based on real-world data. Not only does KBSF solve problems that had never been solved before, but it also significantly outperforms other state-of-the-art reinforcement learning algorithms on the tasks studied.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2372–2441},
numpages = {70},
keywords = {Markov decision processes, stochastic factorization, reinforcement learning, kernel-based approximation, dynamic programming}
}

@article{10.5555/2946645.3007019,
author = {Ghavamzadeh, Mohammad and Engel, Yaakov and Valko, Michal},
title = {Bayesian Policy Gradient and Actor-Critic Algorithms},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Many conventional policy gradient methods use Monte-Carlo techniques to estimate this gradient. The policy is improved by adjusting the parameters in the direction of the gradient estimate. Since Monte-Carlo methods tend to have high variance, a large number of samples is required to attain accurate estimates, resulting in slow convergence. In this paper, we first propose a Bayesian framework for policy gradient, based on modeling the policy gradient as a Gaussian process. This reduces the number of samples needed to obtain accurate gradient estimates. Moreover, estimates of the natural gradient as well as a measure of the uncertainty in the gradient estimates, namely, the gradient covariance, are provided at little extra cost. Since the proposed Bayesian framework considers system trajectories as its basic observable unit, it does not require the dynamics within trajectories to be of any particular form, and thus, can be easily extended to partially observable problems. On the downside, it cannot take advantage of the Markov property when the system is Markovian.To address this issue, we proceed to supplement our Bayesian policy gradient framework with a new actor-critic learning model in which a Bayesian class of non-parametric critics, based on Gaussian process temporal difference learning, is used. Such critics model the action-value function as a Gaussian process, allowing Bayes' rule to be used in computing the posterior distribution over action-value functions, conditioned on the observed data. Appropriate choices of the policy parameterization and of the prior covariance (kernel) between action-values allow us to obtain closed-form expressions for the posterior distribution of the gradient of the expected return with respect to the policy parameters. We perform detailed experimental comparisons of the proposed Bayesian policy gradient and actor-critic algorithms with classic Monte-Carlo based policy gradient methods, as well as with each other, on a number of reinforcement learning problems.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2319–2371},
numpages = {53},
keywords = {reinforcement learning, policy gradient methods, Bayesian inference, Gaussian processes, actor-critic algorithms}
}

@article{10.5555/2946645.2946710,
author = {\v{Z}bontar, Jure and LeCun, Yann},
title = {Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for this task: one tuned for speed, the other for accuracy. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2287–2318},
numpages = {32},
keywords = {matching cost, similarity learning, convolutional neural networks, supervised learning, stereo}
}

@article{10.5555/2946645.2946709,
author = {Adi, Yossi and Keshet, Joseph},
title = {StructED: Risk Minimization in Structured Prediction},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Structured tasks are distinctive: each task has its own measure of performance, such as the word error rate in speech recognition, the BLEU score in machine translation, the NDCG score in information retrieval, or the intersection-over-union score in visual object segmentation. This paper presents STRUCTED, a software package for learning structured prediction models with training methods that aimed at optimizing the task measure of performance. The package was written in Java and released under the MIT license. It can be downloaded from http://adiyoss.github.io/StructED/.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2282–2286},
numpages = {5},
keywords = {direct loss minimization, structural SVM, CRF, structured prediction}
}

@article{10.5555/2946645.2946708,
author = {Andresen, Andreas and Spokoiny, Vladimir},
title = {Convergence of an Alternating Maximization Procedure},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We derive two convergence results for a sequential alternating maximization procedure to approximate the maximizer of random functionals such as the realized log likelihood in MLE estimation. We manage to show that the sequence attains the same deviation properties as shown for the profile M-estimator by Andresen and Spokoiny (2013), that means a finite sample Wilks and Fisher theorem. Further under slightly stronger smoothness constraints on the random functional we can show nearly linear convergence to the global maximizer if the starting point for the procedure is well chosen.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2229–2281},
numpages = {53},
keywords = {profile maximum likelihood, M-estimation, alternating maximization, semiparametric, EM-algorithm, alternating minimization, local concentration, local linear approximation}
}

@article{10.5555/2946645.2946707,
author = {Biau, G\'{e}rard and Bleakley, Kevin and Cadre, Beno\^{\i}t},
title = {The Statistical Performance of Collaborative Inference},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The statistical analysis of massive and complex data sets will require the development of algorithms that depend on distributed computing and collaborative inference. Inspired by this, we propose a collaborative framework that aims to estimate the unknown mean θ of a random variable X. In the model we present, a certain number of calculation units, distributed across a communication network represented by a graph, participate in the estimation of θ by sequentially receiving independent data from X while exchanging messages via a stochastic matrix A defined over the graph. We give precise conditions on the matrix A under which the statistical precision of the individual units is comparable to that of a (gold standard) virtual centralized estimate, even though each unit does not have access to all of the data. We show in particular the fundamental role played by both the non-trivial eigenvalues of A and the Ramanujan class of expander graphs, which provide remarkable performance for moderate algorithmic cost.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2200–2228},
numpages = {29},
keywords = {collaborative estimation, stochastic matrix, graph theory, Ramanujan graph, complexity, distributed computing}
}

@article{10.5555/2946645.2946706,
author = {Mokhtari, Aryan and Ribeiro, Alejandro},
title = {DSA: Decentralized Double Stochastic Averaging Gradient Algorithm},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {This paper considers optimization problems where nodes of a network have access to summands of a global objective. Each of these local objectives is further assumed to be an average of a finite set of functions. The motivation for this setup is to solve large scale machine learning problems where elements of the training set are distributed to multiple computational elements. The decentralized double stochastic averaging gradient (DSA) algorithm is proposed as a solution alternative that relies on: (i) The use of local stochastic averaging gradients. (ii) Determination of descent steps as differences of consecutive stochastic averaging gradients. Strong convexity of local functions and Lipschitz continuity of local gradients is shown to guarantee linear convergence of the sequence generated by DSA in expectation. Local iterates are further shown to approach the optimal argument for almost all realizations. The expected linear convergence of DSA is in contrast to the sublinear rate characteristic of existing methods for decentralized stochastic optimization. Numerical experiments on a logistic regression problem illustrate reductions in convergence time and number of feature vectors processed until convergence relative to these other alternatives.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2165–2199},
numpages = {35},
keywords = {logistic regression, large-scale optimization, stochastic averaging gradient, stochastic optimization, linear convergence, decentralized optimization}
}

@article{10.5555/2946645.2946705,
author = {Bhaskar, Sonia A.},
title = {Probabilistic Low-Rank Matrix Completion from Quantized Measurements},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We consider the recovery of a low rank real-valued matrix M given a subset of noisy discrete (or quantized) measurements. Such problems arise in several applications such as collaborative filtering, learning and content analytics, and sensor network localization. We consider constrained maximum likelihood estimation of M, under a constraint on the entry-wise infinity-norm of M and an exact rank constraint. We provide upper bounds on the Frobenius norm of matrix estimation error under this model. Previous theoretical investigations have focused on binary (1-bit) quantizers, and been based on convex relaxation of the rank. Compared to the existing binary results, our performance upper bound has faster convergence rate with matrix dimensions when the fraction of revealed observations is fixed. We also propose a globally convergent optimization algorithm based on low rank factorization of M and validate the method on synthetic and real data, with improved performance over previous methods.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2131–2164},
numpages = {34},
keywords = {constrained maximum likelihood, convex optimization, matrix completion, quantization, collaborative filtering}
}

@article{10.5555/2946645.2946704,
author = {Ganin, Yaroslav and Ustinova, Evgeniya and Ajakan, Hana and Germain, Pascal and Larochelle, Hugo and Laviolette, Fran\c{c}ois and Marchand, Mario and Lempitsky, Victor},
title = {Domain-Adversarial Training of Neural Networks},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We introduce a new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions. Our approach is directly inspired by the theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on features that cannot discriminate between the training (source) and test (target) domains.The approach implements this idea in the context of neural network architectures that are trained on labeled data from the source domain and unlabeled data from the target domain (no labeled target-domain data is necessary). As the training progresses, the approach promotes the emergence of features that are (i) discriminative for the main learning task on the source domain and (ii) indiscriminate with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation and stochastic gradient descent, and can thus be implemented with little effort using any of the deep learning packages.We demonstrate the success of our approach for two distinct classification problems (document sentiment analysis and image classification), where state-of-the-art domain adaptation performance on standard benchmarks is achieved. We also validate the approach for descriptor learning task in the context of person re-identification application.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2096–2030},
numpages = {35},
keywords = {sentiment analysis, image classification, representation learning, deep learning, domain adaptation, synthetic data, person re-identification, neural network}
}

@article{10.5555/2946645.2946703,
author = {Shah, Nihar B. and Balakrishnan, Sivaraman and Bradley, Joseph and Parekh, Abhay and Ramchandran, Kannan and Wainwright, Martin J.},
title = {Estimation from Pairwise Comparisons: Sharp Minimax Bounds with Topology Dependence},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Data in the form of pairwise comparisons arises in many domains, including preference elicitation, sporting competitions, and peer grading among others. We consider parametric ordinal models for such pairwise comparison data involving a latent vector w* ε Rd that represents the "qualities" of the d items being compared; this class of models includes the two most widely used parametric models|the Bradley-Terry-Luce (BTL) and the Thurstone models. Working within a standard minimax framework, we provide tight upper and lower bounds on the optimal error in estimating the quality score vector w* under this class of models. The bounds depend on the topology of the comparison graph induced by the subset of pairs being compared, via the spectrum of the Laplacian of the comparison graph. Thus, in settings where the subset of pairs may be chosen, our results provide principled guidelines for making this choice. Finally, we compare these error rates to those under cardinal measurement models and show that the error rates in the ordinal and cardinal settings have identical scalings apart from constant pre-factors.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2049–2095},
numpages = {47},
keywords = {ranking, crowdsourcing, topology, pairwise comparisons, graph}
}

@article{10.5555/2946645.2946702,
author = {Niinim\"{a}ki, Teppo and Parviainen, Pekka and Koivisto, Mikko},
title = {Structure Discovery in Bayesian Networks by Sampling Partial Orders},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We present methods based on Metropolis-coupled Markov chain Monte Carlo (MC3) and annealed importance sampling (AIS) for estimating the posterior distribution of Bayesian networks. The methods draw samples from an appropriate distribution of partial orders on the nodes, continued by sampling directed acyclic graphs (DAGs) conditionally on the sampled partial orders. We show that the computations needed for the sampling algorithms are feasible as long as the encountered partial orders have relatively few down-sets. While the algorithms assume suitable modularity properties of the priors, arbitrary priors can be handled by dividing the importance weight of each sampled DAG by the number of topological sorts it has|we give a practical dynamic programming algorithm to compute these numbers. Our empirical results demonstrate that the presented partial-order-based samplers are superior to previous Markov chain Monte Carlo methods, which sample DAGs either directly or via linear orders on the nodes. The results also suggest that the convergence rate of the estimators based on AIS are competitive to those of MC3. Thus AIS is the preferred method, as it enables easier large-scale parallelization and, in addition, supplies good probabilistic lower bound guarantees for the marginal likelihood of the model.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {2002–2048},
numpages = {47},
keywords = {Markov chain Monte Carlo, directed acyclic graph, linear extension, fast zeta transform, annealed importance sampling}
}

@article{10.5555/2946645.2946701,
author = {Silva, Ricardo and Evans, Robin},
title = {Causal Inference through a Witness Protection Program},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {One of the most fundamental problems in causal inference is the estimation of a causal effect when treatment and outcome are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest "weak" paths in an unknown causal graph. The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that imply conditional independencies but do not rule out the existence of confounding causal paths. The output is a posterior distribution over bounds on the average causal effect via a linear programming approach and Bayesian inference. We claim this approach should be used in regular practice as a complement to other tools in observational studies.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1949–2001},
numpages = {53},
keywords = {instrumental variables, causal inference, Bayesian inference, linear programming}
}

@article{10.5555/2946645.2946700,
author = {Ivanoff, St\'{e}phane and Picard, Franck and Rivoirard, Vincent},
title = {Adaptive Lasso and Group-Lasso for Functional Poisson Regression},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {High dimensional Poisson regression has become a standard framework for the analysis of massive counts datasets. In this work we estimate the intensity function of the Poisson regression model by using a dictionary approach, which generalizes the classical basis approach, combined with a Lasso or a group-Lasso procedure. Selection depends on penalty weights that need to be calibrated. Standard methodologies developed in the Gaussian framework can not be directly applied to Poisson models due to heteroscedasticity. Here we provide data-driven weights for the Lasso and the group-Lasso derived from concentration inequalities adapted to the Poisson case. We show that the associated Lasso and group-Lasso procedures satisfy fast and slow oracle inequalities. Simulations are used to assess the empirical performance of our procedure, and an original application to the analysis of Next Generation Sequencing data is provided.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1903–1948},
numpages = {46},
keywords = {concentration, calibration, adaptive group-lasso, functional poisson regression, adaptive lasso}
}

@article{10.5555/2946645.2946699,
author = {Oates, Chris. J. and Smith, Jim Q. and Mukherjee, Sach},
title = {Estimating Causal Structure Using Conditional DAG Models},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {This paper considers inference of causal structure in a class of graphical models called conditional DAGs. These are directed acyclic graph (DAG) models with two kinds of variables, primary and secondary. The secondary variables are used to aid in the estimation of the structure of causal relationships between the primary variables. We prove that, under certain assumptions, such causal structure is identifable from the joint observational distribution of the primary and secondary variables. We give causal semantics for the model class, put forward a score-based approach for estimation and establish consistency results. Empirical results demonstrate gains compared with formulations that treat all variables on an equal footing, or that ignore secondary variables. The methodology is motivated by applications in biology that involve multiple data types and is illustrated here using simulated data and in an analysis of molecular data from the Cancer Genome Atlas.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1880–1903},
numpages = {24},
keywords = {data integration, instrumental variables, graphical models, causal inference, directed acyclic graphs}
}

@article{10.5555/2946645.2946698,
author = {Pilanci, Mert and Wainwright, Martin J.},
title = {Iterative Hessian Sketch: Fast and Accurate Solution Approximation for Constrained Least-Squares},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We study randomized sketching methods for approximately solving least-squares problem with a general convex constraint. The quality of a least-squares approximation can be assessed in different ways: either in terms of the value of the quadratic objective function (cost approximation), or in terms of some distance measure between the approximate minimizer and the true minimizer (solution approximation). Focusing on the latter criterion, our first main result provides a general lower bound on any randomized method that sketches both the data matrix and vector in a least-squares problem; as a surprising consequence, the most widely used least-squares sketch is sub-optimal for solution approximation. We then present a new method known as the iterative Hessian sketch, and show that it can be used to obtain approximations to the original least-squares problem using a projection dimension proportional to the statistical complexity of the least-squares minimizer, and a logarithmic number of iterations. We illustrate our general theory with simulations for both unconstrained and constrained versions of least-squares, including l1-regularization and nuclear norm constraints. We also numerically demonstrate the practicality of our approach in a real face expression classification experiment.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1842–1879},
numpages = {38},
keywords = {Lasso, information theory, convex optimization, random projection, low-rank approximation}
}

@article{10.5555/2946645.2946697,
author = {Gonen, Alon and Rosenbaum, Dan and Eldar, Yonina C. and Shalev-Shwartz, Shai},
title = {Subspace Learning with Partial Information},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The goal of subspace learning is to find a k-dimensional subspace of Rd, such that the expected squared distance between instance vectors and the subspace is as small as possible. In this paper we study subspace learning in a partial information setting, in which the learner can only observe r ≤ d attributes from each instance vector. We propose several efficient algorithms for this task, and analyze their sample complexity.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1821–1841},
numpages = {21},
keywords = {principal components analysis, learning with partial information, learning theory, budgeted learning, statistical learning}
}

@article{10.5555/2946645.2946696,
author = {Wang, Ziteng and Jin, Chi and Fan, Kai and Zhang, Jiaqi and Huang, Junliang and Zhong, Yiqiao and Wang, Liwei},
title = {Differentially Private Data Releasing for Smooth Queries},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {In the past few years, differential privacy has become a standard concept in the area of privacy. One of the most important problems in this field is to answer queries while preserving differential privacy. In spite of extensive studies, most existing work on differentially private query answering assumes the data are discrete (i.e., in {0; 1}d) and focuses on queries induced by Boolean functions. In real applications however, continuous data are at least as common as binary data. Thus, in this work we explore a less studied topic, namely, differential privately query answering for continuous data with continuous function. As a first step towards the continuous case, we study a natural class of linear queries on continuous data which we refer to as smooth queries. A linear query is said to be K-smooth if it is specified by a function defined on [-1; 1]d whose partial derivatives up to order K are all bounded. We develop two ε-differentially private mechanisms which are able to answer all smooth queries. The first mechanism outputs a summary of the database and can then give answers to the queries. The second mechanism is an improvement of the first one and it outputs a synthetic database. The two mechanisms both achieve an accuracy of O(n-K/2d+K/ε). Here we assume that the dimension d is a constant. It turns out that even in this parameter setting (which is almost trivial in the discrete case), using existing discrete mechanisms to answer the smooth queries is difficult and requires more noise. Our mechanisms are based on L∞-approximation of (transformed) smooth functions by low-degree even trigonometric polynomials with uniformly bounded coefficients. We also develop practically efficient variants of the mechanisms with promising experimental results.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1779–1820},
numpages = {42},
keywords = {differential privacy, smooth queries, synthetic dataset}
}

@article{10.5555/2946645.2946695,
author = {Chen, Wei and Wang, Yajun and Yuan, Yang and Wang, Qinshi},
title = {Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We define a general framework for a large class of combinatorial multi-armed bandit (CMAB) problems, where subsets of base arms with unknown distributions form super arms. In each round, a super arm is played and the base arms contained in the super arm are played and their outcomes are observed. We further consider the extension in which more base arms could be probabilistically triggered based on the outcomes of already triggered arms. The reward of the super arm depends on the outcomes of all played arms, and it only needs to satisfy two mild assumptions, which allow a large class of nonlinear reward instances. We assume the availability of an offline (α β)-approximation oracle that takes the means of the outcome distributions of arms and outputs a super arm that with probability β generates an α fraction of the optimal expected reward. The objective of an online learning algorithm for CMAB is to minimize (α β)-approximation regret, which is the difference in total expected reward between the αβ fraction of expected reward when always playing the optimal super arm, and the expected reward of playing super arms according to the algorithm. We provide CUCB algorithm that achieves O(log n) distribution-dependent regret, where n is the number of rounds played, and we further provide distribution-independent bounds for a large class of reward functions. Our regret analysis is tight in that it matches the bound of UCB1 algorithm (up to a constant factor) for the classical MAB problem, and it significantly improves the regret bound in an earlier paper on combinatorial bandits with linear rewards. We apply our CMAB framework to two new applications, probabilistic maximum coverage (PMC) for online advertising and social in uence maximization for viral marketing, both having nonlinear reward structures. In particular, application to social in uence maximization requires our extension on probabilistically triggered arms.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1746–1778},
numpages = {33},
keywords = {online advertising, online learning, combinatorial multi-armed bandit, social influence maximization, upper confidence bound}
}

@article{10.5555/2946645.2946694,
author = {Wang, Shusen and Luo, Luo and Zhang, Zhihua},
title = {SPSD Matrix Approximation Vis Column Selection: Theories, Algorithms, and Extensions},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Symmetric positive semidefinite (SPSD) matrix approximation is an important problem with applications in kernel methods. However, existing SPSD matrix approximation methods such as the Nystr\"{o}m method only have weak error bounds. In this paper we conduct in-depth studies of an SPSD matrix approximation model and establish strong relative-error bounds. We call it the prototype model for it has more efficient and effective extensions, and some of its extensions have high scalability. Though the prototype model itself is not suitable for large-scale data, it is still useful to study its properties, on which the analysis of its extensions relies.This paper offers novel theoretical analysis, efficient algorithms, and a highly accurate extension. First, we establish a lower error bound for the prototype model and improve the error bound of an existing column selection algorithm to match the lower bound. In this way, we obtain the first optimal column selection algorithm for the prototype model. We also prove that the prototype model is exact under certain conditions. Second, we develop a simple column selection algorithm with a provable error bound. Third, we propose a so-called spectral shifting model to make the approximation more accurate when the eigenvalues of the matrix decay slowly, and the improvement is theoretically quantified. The spectral shifting method can also be applied to improve other SPSD matrix approximation models.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1697–1745},
numpages = {49},
keywords = {matrix approximation, kernel methods, the Nystr\"{o}m method, matrix factorization, spectral shifting}
}

@article{10.5555/2946645.2946693,
author = {Muandet, Krikamol and Sriperumbudur, Bharath and Fukumizu, Kenji and Gretton, Arthur and Sch\"{o}lkopf, Bernhard},
title = {Kernel Mean Shrinkage Estimators},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {A mean function in a reproducing kernel Hilbert space (RKHS), or a kernel mean, is central to kernel methods in that it is used by many classical algorithms such as kernel principal component analysis, and it also forms the core inference step of modern kernel methods that rely on embedding probability distributions in RKHSs. Given a finite sample, an empirical average has been used commonly as a standard estimator of the true kernel mean. Despite a widespread use of this estimator, we show that it can be improved thanks to the well-known Stein phenomenon. We propose a new family of estimators called kernel mean shrinkage estimators (KMSEs), which benefit from both theoretical justifications and good empirical performance. The results demonstrate that the proposed estimators outperform the standard one, especially in a "large d, small n" paradigm.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1656–1696},
numpages = {41},
keywords = {Tikhonov regularization, Stein effect, shrinkage estimators, James-Stein estimators, kernel methods, kernel mean, covariance operator}
}

@article{10.5555/2946645.2946692,
author = {Lu, Jing and Hoi, Steven C. H. and Wang, Jialei and Zhao, Peilin and Liu, Zhi-Yong},
title = {Large Scale Online Kernel Learning},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {In this paper, we present a new framework for large scale online kernel learning, making kernel methods efficient and scalable for large-scale online learning applications. Unlike the regular budget online kernel learning scheme that usually uses some budget maintenance strategies to bound the number of support vectors, our framework explores a completely different approach of kernel functional approximation techniques to make the subsequent online learning task efficient and scalable. Specifically, we present two different online kernel machine learning algorithms: (i) Fourier Online Gradient Descent (FOGD) algorithm that applies the random Fourier features for approximating kernel functions; and (ii) Nystr\"{o}m Online Gradient Descent (NOGD) algorithm that applies the Nystr\"{o}m method to approximate large kernel matrices. We explore these two approaches to tackle three online learning tasks: binary classification, multi-class classification, and regression. The encouraging results of our experiments on large-scale datasets validate the effectiveness and efficiency of the proposed algorithms, making them potentially more practical than the family of existing budget online kernel learning approaches.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1613–1655},
numpages = {43},
keywords = {online learning, large scale machine learning, kernel approximation}
}

@article{10.5555/2946645.2946691,
author = {Abdallah, Sherief and Kaisers, Michael},
title = {Addressing Environment Non-Stationarity by Repeating Q-Learning Updates},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Q-learning (QL) is a popular reinforcement learning algorithm that is guaranteed to converge to optimal policies in Markov decision processes. However, QL exhibits an artifact: in expectation, the effective rate of updating the value of an action depends on the probability of choosing that action. In other words, there is a tight coupling between the learning dynamics and underlying execution policy. This coupling can cause performance degradation in noisy non-stationary environments.Here, we introduce Repeated Update Q-learning (RUQL), a learning algorithm that resolves the undesirable artifact of Q-learning while maintaining simplicity. We analyze the similarities and differences between RUQL, QL, and the closest state-of-the-art algorithms theoretically. Our analysis shows that RUQL maintains the convergence guarantee of QL in stationary environments, while relaxing the coupling between the execution policy and the learning dynamics. Experimental results confirm the theoretical insights and show how RUQL outperforms both QL and the closest state-of-the-art algorithms in noisy non-stationary environments.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1582–1612},
numpages = {31},
keywords = {non-stationary environments, multi-agent learning, Q-learning, reinforcement learning}
}

@article{10.5555/2946645.2946690,
author = {Doundefinedan, \"{U}r\"{u}n and Glasmachers, Tobias and Igel, Christian},
title = {A Unified View on Multi-Class Support Vector Classification},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {A unified view on multi-class support vector machines (SVMs) is presented, covering most prominent variants including the one-vs-all approach and the algorithms proposed by Weston & Watkins, Crammer & Singer, Lee, Lin, & Wahba, and Liu & Yuan. The unification leads to a template for the quadratic training problems and new multi-class SVM formulations. Within our framework, we provide a comparative analysis of the various notions of multi-class margin and margin-based loss. In particular, we demonstrate limitations of the loss function considered, for instance, in the Crammer & Singer machine.We analyze Fisher consistency of multi-class loss functions and universal consistency of the various machines. On the one hand, we give examples of SVMs that are, in a particular hyperparameter regime, universally consistent without being based on a Fisher consistent loss. These include the canonical extension of SVMs to multiple classes as proposed by Weston & Watkins and Vapnik as well as the one-vs-all approach. On the other hand, it is demonstrated that machines based on Fisher consistent loss functions can fail to identify proper decision boundaries in low-dimensional feature spaces.We compared the performance of nine different multi-class SVMs in a thorough empirical study. Our results suggest to use the Weston & Watkins SVM, which can be trained comparatively fast and gives good accuracies on benchmark functions. If training time is a major concern, the one-vs-all approach is the method of choice.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1550–1831},
numpages = {282},
keywords = {consistency, support vector machines, multi-class classification}
}

@article{10.5555/2946645.2946689,
author = {Mart\'{\i}nez, Ana M. and Webb, Geoffrey I. and Chen, Shenglei and Zaidi, Nayyar A.},
title = {Scalable Learning of Bayesian Network Classifiers},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Ever increasing data quantity makes ever more urgent the need for highly scalable learners that have good classification performance. Therefore, an out-of-core learner with excellent time and space complexity, along with high expressivity (that is, capacity to learn very complex multivariate probability distributions) is extremely desirable. This paper presents such a learner. We propose an extension to the k-dependence Bayesian classifier (KDB) that discriminatively selects a sub-model of a full KDB classifier. It requires only one additional pass through the training data, making it a three-pass learner. Our extensive experimental evaluation on 16 large data sets reveals that this out-of-core algorithm achieves competitive classification performance, and substantially better training and classification time than state-of-the-art in-core learners such as random forest and linear and non-linear logistic regression.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1515–1549},
numpages = {35},
keywords = {out-of-core learning, scalable Bayesian classification, big data, feature selection}
}

@article{10.5555/2946645.2946688,
author = {Arias-Castro, Ery and Mason, David and Pelletier, Bruno},
title = {On the Estimation of the Gradient Lines of a Density and the Consistency of the Mean-Shift Algorithm},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We consider the problem of estimating the gradient lines of a density, which can be used to cluster points sampled from that density, for example via the mean-shift algorithm of Fukunaga and Hostetler (1975). We prove general convergence bounds that we then specialize to kernel density estimation.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1487–1514},
numpages = {28},
keywords = {density estimation, nonparametric clustering, gradient lines, mean-shift}
}

@article{10.5555/2946645.2946687,
author = {Damianou, Andreas C. and Titsias, Michalis K. and Lawrence, Neil D.},
title = {Variational Inference for Latent Variables and Uncertain Inputs in Gaussian Processes},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The Gaussian process latent variable model (GP-LVM) provides a flexible approach for non-linear dimensionality reduction that has been widely applied. However, the current approach for training GP-LVMs is based on maximum likelihood, where the latent projection variables are maximised over rather than integrated out. In this paper we present a Bayesian method for training GP-LVMs by introducing a non-standard variational inference framework that allows to approximately integrate out the latent variables and subsequently train a GP-LVM by maximising an analytic lower bound on the exact marginal likelihood. We apply this method for learning a GP-LVM from i.i.d. observations and for learning non-linear dynamical systems where the observations are temporally correlated. We show that a benefit of the variational Bayesian procedure is its robustness to overfitting and its ability to automatically select the dimensionality of the non-linear latent space. The resulting framework is generic, flexible and easy to extend for other purposes, such as Gaussian process regression with uncertain or partially missing inputs. We demonstrate our method on synthetic data and standard machine learning benchmarks, as well as challenging real world datasets, including high resolution video data.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1425–1486},
numpages = {62},
keywords = {variational inference, latent variable models, Gaussian processes, dynamical systems, uncertain inputs}
}

@article{10.5555/2946645.2946686,
author = {Luttinen, Jaakko},
title = {BayesPy: Variational Bayesian Inference in Python},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {BayesPy is an open-source Python software package for performing variational Bayesian inference. It is based on the variational message passing framework and supports conjugate exponential family models. By removing the tedious task of implementing the variational Bayesian update equations, the user can construct models faster and in a less error-prone way. Simple syntax, flexible model construction and efficient inference make BayesPy suitable for both average and expert Bayesian users. It also supports some advanced methods such as stochastic and collapsed variational inference.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1419–1424},
numpages = {6},
keywords = {variational Bayes, Python, probabilistic programming}
}

@article{10.5555/2946645.2946685,
author = {Zhang, Chong and Liu, Yufeng and Wu, Yichao},
title = {On Quantile Regression in Reproducing Kernel Hilbert Spaces with the Data Sparsity Constraint},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {For spline regressions, it is well known that the choice of knots is crucial for the performance of the estimator. As a general learning framework covering the smoothing splines, learning in a Reproducing Kernel Hilbert Space (RKHS) has a similar issue. However, the selection of training data points for kernel functions in the RKHS representation has not been carefully studied in the literature. In this paper we study quantile regression as an example of learning in a RKHS. In this case, the regular squared norm penalty does not perform training data selection. We propose a data sparsity constraint that imposes thresholding on the kernel function coefficients to achieve a sparse kernel function representation. We demonstrate that the proposed data sparsity method can have competitive prediction performance for certain situations, and have comparable performance in other cases compared to that of the traditional squared norm penalty. Therefore, the data sparsity method can serve as a competitive alternative to the squared norm penalty method. Some theoretical properties of our proposed method using the data sparsity constraint are obtained. Both simulated and real data sets are used to demonstrate the usefulness of our data sparsity constraint.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1374–1418},
numpages = {45},
keywords = {smoothing, sparsity, kernel learning, regression, rademacher complexity}
}

@article{10.5555/2946645.2946684,
author = {Levine, Sergey and Finn, Chelsea and Darrell, Trevor and Abbeel, Pieter},
title = {End-to-End Training of Deep Visuomotor Policies},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Policy search methods can allow robots to learn control policies for a wide range of tasks, but practical applications of policy search often require hand-engineered components for perception, state estimation, and low-level control. In this paper, we aim to answer the following question: does training the perception and control systems jointly end-to-end provide better performance than training each component separately? To this end, we develop a method that can be used to learn policies that map raw image observations directly to torques at the robot's motors. The policies are represented by deep convolutional neural networks (CNNs) with 92,000 parameters, and are trained using a guided policy search method, which transforms policy search into supervised learning, with supervision provided by a simple trajectory-centric reinforcement learning method. We evaluate our method on a range of real-world manipulation tasks that require close coordination between vision and control, such as screwing a cap onto a bottle, and present simulated comparisons to a range of prior policy search methods.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1334–1373},
numpages = {40},
keywords = {neural networks, vision, optimal control, reinforcement learning}
}

@article{10.5555/2946645.2946683,
author = {Hanneke, Steve},
title = {The Optimal Sample Complexity OF PAC Learning},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {This work establishes a new upper bound on the number of samples sufficient for PAC learning in the realizable case. The bound matches known lower bounds up to numerical constant factors. This solves a long-standing open problem on the sample complexity of PAC learning. The technique and analysis build on a recent breakthrough by Hans Simon.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1319–1333},
numpages = {15},
keywords = {minimax analysis, learning algorithm, statistical learning theory, sample complexity, PAC learning}
}

@article{10.5555/2946645.2946682,
author = {Zhang, Shiliang and Jiang, Hui and Dai, Lirong},
title = {Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Learn Neural Networks},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {In this paper, we propose a novel model for high-dimensional data, called the Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework. The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data. More interestingly, we have shown the proposed HOPE models are closely related to neural networks (NNs) in a sense that each hidden layer can be reformulated as a HOPE model. As a result, the HOPE framework can be used as a novel tool to probe why and how NNs work, more importantly, to learn NNs in either supervised or unsupervised ways. In this work, we have investigated the HOPE framework to learn NNs for several standard tasks, including image recognition on MNIST and speech recognition on TIMIT. Experimental results have shown that the HOPE framework yields significant performance gains over the current state-of-the-art methods in various types of NN learning problems, including unsupervised feature learning, supervised or semi-supervised learning.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1286–1318},
numpages = {33},
keywords = {mixture models, PCA, von Mises-Fisher (vMF) model, unsupervised learning, orthogonal projection, neural networks}
}

@article{10.5555/2946645.2946681,
author = {Pfeuffer, Julianus and Serang, Oliver},
title = {A Bounded P-Norm Approximation of Max-Convolution for Sub-Quadratic Bayesian Inference on Additive Factors},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Max-convolution is an important problem closely resembling standard convolution; as such, max-convolution occurs frequently across many fields. Here we extend the method with fastest known worst-case runtime, which can be applied to nonnegative vectors by numerically approximating the Chebyshev norm || undefined ||∞, and use this approach to derive two numerically stable methods based on the idea of computing p-norms via fast convolution: The first method proposed, with runtime in O(k log(k) log(log(k))) (which is less than 18k log(k) for any vectors that can be practically realized), uses the p-norm as a direct approximation of the Chebyshev norm. The second approach proposed, with runtime in O(k log(k)) (although in practice both perform similarly), uses a novel null space projection method, which extracts information from a sequence of p-norms to estimate the maximum value in the vector (this is equivalent to querying a small number of moments from a distribution of bounded support in order to estimate the maximum). The p-norm approaches are compared to one another and are shown to compute an approximation of the Viterbi path in a hidden Markov model where the transition matrix is a Toeplitz matrix; the runtime of approximating the Viterbi path is thus reduced from O(nk2) steps to O(nk log(k)) steps in practice, and is demonstrated by inferring the U.S. unemployment rate from the S&P 500 stock index.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1247–1285},
numpages = {39},
keywords = {fast Fourier transform, max-convolution, p-norm, null space projection, polynomial matrix, Lp space, Bayesian inference, hidden Markov model, maximum a posteriori}
}

@article{10.5555/2946645.2946680,
author = {Li, Bin and Sahoo, Doyen and Hoi, Steven C. H.},
title = {OLPS: A Toolbox for on-Line Portfolio Selection},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {On-line portfolio selection is a practical financial engineering problem, which aims to sequentially allocate capital among a set of assets in order to maximize long-term return. In recent years, a variety of machine learning algorithms have been proposed to address this challenging problem, but no comprehensive open-source toolbox has been released for various reasons. This article presents the first open-source toolbox for "On-Line Portfolio Selection" (OLPS), which implements a collection of classical and state-of-the-art strategies powered by machine learning algorithms. We hope that OLPS can facilitate the development of new learning methods and enable the performance benchmarking and comparisons of different strategies. OLPS is an open-source project released under Apache License (version 2.0), which is available at https://github.com/OLPS/ or http://OLPS.stevenhoi.org/.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1242–1246},
numpages = {5},
keywords = {on-line portfolio selection, simulation, online learning, trading system}
}

@article{10.5555/2946645.2946679,
author = {Meng, Xiangrui and Bradley, Joseph and Yavuz, Burak and Sparks, Evan and Venkataraman, Shivaram and Liu, Davies and Freeman, Jeremy and Tsai, DB and Amde, Manish and Owen, Sean and Xin, Doris and Xin, Reynold and Franklin, Michael J. and Zadeh, Reza and Zaharia, Matei and Talwalkar, Ameet},
title = {MLlib: Machine Learning in Apache Spark},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark's open-source distributed machine learning library. MLLIB provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLLIB supports several languages and provides a high-level API that leverages Spark's rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLLIB has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1235–1241},
numpages = {7},
keywords = {apache spark, distributed algorithms, scalable machine learning}
}

@article{10.5555/2946645.2946678,
author = {Gon\c{c}alves, Andr\'{e} R. and Von Zuben, Fernando J. and Banerjee, Arindam},
title = {Multi-Task Sparse Structure Learning with Gaussian Copula Models},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Multi-task learning (MTL) aims to improve generalization performance by learning multiple related tasks simultaneously. While sometimes the underlying task relationship structure is known, often the structure needs to be estimated from data at hand. In this paper, we present a novel family of models for MTL, applicable to regression and classification problems, capable of learning the structure of tasks relationship. In particular, we consider a joint estimation problem of the tasks relationship structure and the individual task parameters, which is solved using alternating minimization. The task relationship revealed by structure learning is founded on recent advances in Gaussian graphical models endowed with sparse estimators of the precision (inverse covariance) matrix. An extension to include exible Gaussian copula models that relaxes the Gaussian marginal assumption is also proposed. We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark data sets for regression and classification. We also consider the problem of combining Earth System Model (ESM) outputs for better projections of future climate, with focus on projections of temperature by combining ESMs in South and North America, and show that the proposed model outperforms several existing methods for the problem.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1205–1234},
numpages = {30},
keywords = {multi-task learning, structure learning, probabilistic graphical model, Gaussian copula, sparse modeling}
}

@article{10.5555/2946645.2946677,
author = {Mooij, Joris M. and Peters, Jonas and Janzing, Dominik and Zscheischler, Jakob and Sch\"{o}lkopf, Bernhard},
title = {Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {The discovery of causal relationships from purely observational data is a fundamental problem in science. The most elementary form of such a causal discovery problem is to decide whether X causes Y or, alternatively, Y causes X, given joint observations of two variables X,Y. An example is to decide whether altitude causes temperature, or vice versa, given only joint measurements of both variables. Even under the simplifying assumptions of no confounding, no feedback loops, and no selection bias, such bivariate causal discovery problems are challenging. Nevertheless, several approaches for addressing those problems have been proposed in recent years. We review two families of such methods: methods based on Additive Noise Models (ANMs) and Information Geometric Causal Inference (IGCI). We present the benchmark CAUSEEFFECTPAIRS that consists of data for 100 different causee ffect pairs selected from 37 data sets from various domains (e.g., meteorology, biology, medicine, engineering, economy, etc.) and motivate our decisions regarding the "ground truth" causal directions of all pairs. We evaluate the performance of several bivariate causal discovery methods on these real-world benchmark data and in addition on artificially simulated data. Our empirical results on real-world data indicate that certain methods are indeed able to distinguish cause from effect using only purely observational data, although more benchmark data would be needed to obtain statistically significant conclusions. One of the best performing methods overall is the method based on Additive Noise Models that has originally been proposed by Hoyer et al. (2009), which obtains an accuracy of 63 ± 10 % and an AUC of 0.74 ± 0.05 on the real-world benchmark. As the main theoretical contribution of this work we prove the consistency of that method.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1103–1204},
numpages = {102},
keywords = {benchmarks, causal discovery, cause-effect pairs, additive noise, information-geometric causal inference}
}

@article{10.5555/2946645.2946676,
author = {Denis, Fran\c{c}ois and Gybels, Mattias and Habrard, Amaury},
title = {Dimension-Free Concentration Bounds on Hankel Matrices for Spectral Learning},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {Learning probabilistic models over strings is an important issue for many applications. Spectral methods propose elegant solutions to the problem of inferring weighted automata from finite samples of variable-length strings drawn from an unknown target distribution p. These methods rely on a singular value decomposition of a matrix HS, called the empirical Hankel matrix, that records the frequencies of (some of) the observed strings S. The accuracy of the learned distribution depends both on the quantity of information embedded in HS and on the distance between HS and its mean Hp. Existing concentration bounds seem to indicate that the concentration over Hp gets looser with its dimensions, suggesting that it might be necessary to bound the dimensions of HS for learning. We prove new dimensionfree concentration bounds for classical Hankel matrices and several variants, based on prefixes or factors of strings, that are useful for learning. Experiments demonstrate that these bounds are tight and that they significantly improve existing (dimension-dependent) bounds. One consequence of these results is that the spectral learning approach remains consistent even if all the observations are recorded within the empirical matrix.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1071–1102},
numpages = {32},
keywords = {matrix bernstein bounds, probabilistic grammatical inference, rational series, spectral learning, hankel matrices}
}

@article{10.5555/2946645.2946675,
author = {Goudie, Robert J. B. and Mukherjee, Sach},
title = {A Gibbs Sampler for Learning DAGs},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {We propose a Gibbs sampler for structure learning in directed acyclic graph (DAG) models. The standard Markov chain Monte Carlo algorithms used for learning DAGs are random-walk Metropolis-Hastings samplers. These samplers are guaranteed to converge asymptotically but often mix slowly when exploring the large graph spaces that arise in structure learning. In each step, the sampler we propose draws entire sets of parents for multiple nodes from the appropriate conditional distribution. This provides an efficient way to make large moves in graph space, permitting faster mixing whilst retaining asymptotic guarantees of convergence. The conditional distribution is related to variable selection with candidate parents playing the role of covariates or inputs. We empirically examine the performance of the sampler using several simulated and real data examples. The proposed method gives robust results in diverse settings, outperforming several existing Bayesian and frequentist methods. In addition, our empirical results shed some light on the relative merits of Bayesian and constraint-based methods for structure learning.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {1032–1070},
numpages = {39},
keywords = {Gibbs sampling, DAGs, Bayesian networks, variable selection, structure learning, Markov chain Monte Carlo}
}

@article{10.5555/2946645.2946674,
author = {Heller, Ruth and Heller, Yair and Kaufman, Shachar and Brill, Barak and Gorfine, Malka},
title = {Consistent Distribution-Free K-Sample and Independence Tests for Univariate Random Variables},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {1532-4435},
abstract = {A popular approach for testing if two univariate random variables are statistically independent consists of partitioning the sample space into bins, and evaluating a test statistic on the binned data. The partition size matters, and the optimal partition size is data dependent. While for detecting simple relationships coarse partitions may be best, for detecting complex relationships a great gain in power can be achieved by considering finer partitions. We suggest novel consistent distribution-free tests that are based on summation or maximization aggregation of scores over all partitions of a fixed size. We show that our test statistics based on summation can serve as good estimators of the mutual information. Moreover, we suggest regularized tests that aggregate over all partition sizes, and prove those are consistent too. We provide polynomial-time algorithms, which are critical for computing the suggested test statistics efficiently. We show that the power of the regularized tests is excellent compared to existing tests, and almost as powerful as the tests based on the optimal (yet unknown in practice) partition size, in simulations as well as on a real data example.},
journal = {J. Mach. Learn. Res.},
month = jan,
pages = {978–1031},
numpages = {54},
keywords = {two-sample test, HHG R package, mutual information, bivariate distribution, nonparametric test, statistical independence}
}

@article{10.5555/2946645.2946673,
author = {Hern\'{a}ndez-Lobato, Daniel and Morales-Mombiela, Pablo and Lopez-Paz, David and Su\'{a}rez, Alberto},
title = {Non-Linear Causal Inference Using Gaussianity Measures},
year = {2016},
issue_date = {January 2016},
publisher = {JMLR.org},
volume = {17},
number = {1},
issn = {

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr