text:
3462,
author = {Ma, Justin and Saul, Lawrence K. and Savage, Stefan and Voelker, Geoffrey M.},
title = {Identifying Suspicious URLs: An Application of Large-Scale Online Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553462},
doi = {10.1145/1553374.1553462},
abstract = {This paper explores online learning approaches for detecting malicious Web sites (those involved in criminal scams) using lexical and host-based features of the associated URLs. We show that this application is particularly appropriate for online algorithms as the size of the training data is larger than can be efficiently processed in batch and because the distribution of features that typify malicious URLs is changing continuously. Using a real-time system we developed for gathering URL features, combined with a real-time source of labeled URLs from a large Web mail provider, we demonstrate that recently-developed online algorithms can be as accurate as batch techniques, achieving classification accuracies up to 99% over a balanced data set.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {681–688},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553463,
author = {Mairal, Julien and Bach, Francis and Ponce, Jean and Sapiro, Guillermo},
title = {Online Dictionary Learning for Sparse Coding},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553463},
doi = {10.1145/1553374.1553463},
abstract = {Sparse coding---that is, modelling data vectors as sparse linear combinations of basis elements---is widely used in machine learning, neuroscience, signal processing, and statistics. This paper focuses on learning the basis set, also called dictionary, to adapt it to specific data, an approach that has recently proven to be very effective for signal reconstruction and classification in the audio and image processing domains. This paper proposes a new online optimization algorithm for dictionary learning, based on stochastic approximations, which scales up gracefully to large datasets with millions of training samples. A proof of convergence is presented, along with experiments with natural images demonstrating that it leads to faster performance and better dictionaries than classical batch algorithms for both small and large datasets.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {689–696},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553464,
author = {Makino, Takaki},
title = {Proto-Predictive Representation of States with Simple Recurrent Temporal-Difference Networks},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553464},
abstract = {We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observations in partially observable environments. SR-TDNs incorporate the structure of simple recurrent neural networks (SRNs) into temporal-difference (TD) networks to use proto-predictive representation of states. Although they deviate from the principle of predictive representations to ground state representations on observations, they follow the same learning strategy as TD networks, i.e., applying TD-learning to general predictions. Simulation experiments revealed that SR-TDNs can correctly represent states with an incomplete set of core tests (question networks), and consequently, SR-TDNs have better on-line learning capacity than TD networks in various environments.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {697–704},
numpages = {8}
}

@inbook{10.1145/1553374.1553465,
author = {Marlin, Benjamin M. and Murphy, Kevin P.},
title = {Sparse Gaussian Graphical Models with Unknown Block Structure},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553465},
abstract = {Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization methods to find the penalized maximum likelihood estimate. This is similar to performing MAP estimation with a prior that prefers sparse graphs. In this paper, we use the stochastic block model as a prior. This prefer graphs that are blockwise sparse, but unlike previous work, it does not require that the blocks or groups be specified a priori. The resulting problem is no longer convex, but we devise an efficient variational Bayes algorithm to solve it. We show that our method has better test set likelihood on two different datasets (motion capture and gene expression) compared to independent L1, and can match the performance of group L1 using manually created groups.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {705–712},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553466,
author = {Martins, Andr\'{e} F. T. and Smith, Noah A. and Xing, Eric P.},
title = {Polyhedral Outer Approximations with Application to Natural Language Parsing},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553466},
doi = {10.1145/1553374.1553466},
abstract = {Recent approaches to learning structured predictors often require approximate inference for tractability; yet its effects on the learned model are unclear. Meanwhile, most learning algorithms act as if computational cost was constant within the model class. This paper sheds some light on the first issue by establishing risk bounds for max-margin learning with LP relaxed inference and addresses the second issue by proposing a new paradigm that attempts to penalize "time-consuming" hypotheses. Our analysis relies on a geometric characterization of the outer polyhedra associated with the LP relaxation. We then apply these techniques to the problem of dependency parsing, for which a concise LP formulation is provided that handles non-local output features. A significant improvement is shown over arc-factored models.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {713–720},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553467,
author = {McFee, Brian and Lanckriet, Gert},
title = {Partial Order Embedding with Multiple Kernels},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553467},
doi = {10.1145/1553374.1553467},
abstract = {We consider the problem of embedding arbitrary objects (e.g., images, audio, documents) into Euclidean space subject to a partial order over pair-wise distances. Partial order constraints arise naturally when modeling human perception of similarity. Our partial order framework enables the use of graph-theoretic tools to more efficiently produce the embedding, and exploit global structure within the constraint set.We present an embedding algorithm based on semidefinite programming, which can be parameterized by multiple kernels to yield a unified space from heterogeneous features.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {721–728},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553468,
author = {de Mesmay, Fr\'{e}d\'{e}ric and Rimmel, Arpad and Voronenko, Yevgen and P\"{u}schel, Markus},
title = {Bandit-Based Optimization on Graphs with Application to Library Performance Tuning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553468},
abstract = {The problem of choosing fast implementations for a class of recursive algorithms such as the fast Fourier transforms can be formulated as an optimization problem over the language generated by a suitably defined grammar. We propose a novel algorithm that solves this problem by reducing it to maximizing an objective function over the sinks of a directed acyclic graph. This algorithm valuates nodes using Monte-Carlo and grows a subgraph in the most promising directions by considering local maximum k-armed bandits. When used inside an adaptive linear transform library, it cuts down the search time by an order of magnitude compared to the existing algorithm. In some cases, the performance of the implementations found is also increased by up to 10% which is of considerable practical importance since it consequently improves the performance of all applications using the library.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {729–736},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553469,
author = {Mobahi, Hossein and Collobert, Ronan and Weston, Jason},
title = {Deep Learning from Temporal Coherence in Video},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553469},
doi = {10.1145/1553374.1553469},
abstract = {This work proposes a learning method for deep architectures that takes advantage of sequential data, in particular from the temporal coherence that naturally exists in unlabeled video recordings. That is, two successive frames are likely to contain the same object or objects. This coherence is used as a supervisory signal over the unlabeled data, and is used to improve the performance on a supervised task of interest. We demonstrate the effectiveness of this method on some pose invariant object and face recognition tasks.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {737–744},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553470,
author = {Mooij, Joris and Janzing, Dominik and Peters, Jonas and Sch\"{o}lkopf, Bernhard},
title = {Regression by Dependence Minimization and Its Application to Causal Inference in Additive Noise Models},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553470},
abstract = {Motivated by causal inference problems, we propose a novel method for regression that minimizes the statistical dependence between regressors and residuals. The key advantage of this approach to regression is that it does not assume a particular distribution of the noise, i.e., it is non-parametric with respect to the noise distribution. We argue that the proposed regression method is well suited to the task of causal inference in additive noise models. A practical disadvantage is that the resulting optimization problem is generally non-convex and can be difficult to solve. Nevertheless, we report good results on one of the tasks of the NIPS 2008 Causality Challenge, where the goal is to distinguish causes from effects in pairs of statistically dependent variables. In addition, we propose an algorithm for efficiently inferring causal models from observational data for more than two variables. The required number of regressions and independence tests is quadratic in the number of variables, which is a significant improvement over the simple method that tests all possible DAGs.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {745–752},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553471,
author = {Neumann, Gerhard and Maass, Wolfgang and Peters, Jan},
title = {Learning Complex Motions by Sequencing Simpler Motion Templates},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553471},
doi = {10.1145/1553374.1553471},
abstract = {Abstraction of complex, longer motor tasks into simpler elemental movements enables humans and animals to exhibit motor skills which have not yet been matched by robots. Humans intuitively decompose complex motions into smaller, simpler segments. For example when describing simple movements like drawing a triangle with a pen, we can easily name the basic steps of this movement.Surprisingly, such abstractions have rarely been used in artificial motor skill learning algorithms. These algorithms typically choose a new action (such as a torque or a force) at a very fast time-scale. As a result, both policy and temporal credit assignment problem become unnecessarily complex - often beyond the reach of current machine learning methods.We introduce a new framework for temporal abstractions in reinforcement learning (RL), i.e. RL with motion templates. We present a new algorithm for this framework which can learn high-quality policies by making only few abstract decisions.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {753–760},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553472,
author = {Nickisch, Hannes and Seeger, Matthias W.},
title = {Convex Variational Bayesian Inference for Large Scale Generalized Linear Models},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553472},
abstract = {We show how variational Bayesian inference can be implemented for very large generalized linear models. Our relaxation is proven to be a convex problem for any log-concave model. We provide a generic double loop algorithm for solving this relaxation on models with arbitrary super-Gaussian potentials. By iteratively decoupling the criterion, most of the work can be done by solving large linear systems, rendering our algorithm orders of magnitude faster than previously proposed solvers for the same problem. We evaluate our method on problems of Bayesian active learning for large binary classification models, and show how to address settings with many candidates and sequential inclusion steps.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {761–768},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553473,
author = {Nowozin, Sebastian and Jegelka, Stefanie},
title = {Solution Stability in Linear Programming Relaxations: Graph Partitioning and Unsupervised Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553473},
doi = {10.1145/1553374.1553473},
abstract = {We propose a new method to quantify the solution stability of a large class of combinatorial optimization problems arising in machine learning. As practical example we apply the method to correlation clustering, clustering aggregation, modularity clustering, and relative performance significance clustering. Our method is extensively motivated by the idea of linear programming relaxations. We prove that when a relaxation is used to solve the original clustering problem, then the solution stability calculated by our method is conservative, that is, it never overestimates the solution stability of the true, unrelaxed problem. We also demonstrate how our method can be used to compute the entire path of optimal solutions as the optimization problem is increasingly perturbed. Experimentally, our method is shown to perform well on a number of benchmark problems.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {769–776},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553474,
author = {Paisley, John and Carin, Lawrence},
title = {Nonparametric Factor Analysis with Beta Process Priors},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553474},
doi = {10.1145/1553374.1553474},
abstract = {We propose a nonparametric extension to the factor analysis problem using a beta process prior. This beta process factor analysis (BP-FA) model allows for a dataset to be decomposed into a linear combination of a sparse set of factors, providing information on the underlying structure of the observations. As with the Dirichlet process, the beta process is a fully Bayesian conjugate prior, which allows for analytical posterior calculation and straightforward inference. We derive a varia-tional Bayes inference algorithm and demonstrate the model on the MNIST digits and HGDP-CEPH cell line panel datasets.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {777–784},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553475,
author = {Pan, Wei and Torresani, Lorenzo},
title = {Unsupervised Hierarchical Modeling of Locomotion Styles},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553475},
doi = {10.1145/1553374.1553475},
abstract = {This paper describes an unsupervised learning technique for modeling human locomotion styles, such as distinct related activities (e.g. running and striding) or variations of the same motion performed by different subjects. Modeling motion styles requires identifying the common structure in the motions and detecting style-specific characteristics. We propose an algorithm that learns a hierarchical model of styles from unlabeled motion capture data by exploiting the cyclic property of human locomotion. We assume that sequences with the same style contain locomotion cycles generated by noisy, temporally warped versions of a single latent cycle. We model these style-specific latent cycles as random variables drawn from a common "parent" cycle distribution, representing the structure shared by all motions. Given these hierarchical priors, the algorithm learns, in a completely unsupervised fashion, temporally aligned latent cycle distributions, each modeling a specific locomotion style, and computes for each example the style label posterior distribution, the segmentation into cycles, and the temporal warping with respect to the latent cycles. We demonstrate the flexibility of the model on several application problems such as style clustering, animation, style blending, and filling in of missing data.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {785–792},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553476,
author = {Pazis, Jason and Lagoudakis, Michail G.},
title = {Binary Action Search for Learning Continuous-Action Control Policies},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553476},
doi = {10.1145/1553374.1553476},
abstract = {Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-world problems, the most common approach still employed in practice is coarse discretization of the action space. This paper presents a novel method, called Binary Action Search, for realizing continuousaction policies by searching efficiently the entire action range through increment and decrement modifications to the values of the action variables according to an internal binary policy defined over an augmented state space. The proposed approach essentially approximates any continuous action space to arbitrary resolution and can be combined with any discrete-action reinforcement learning algorithm for learning continuous-action policies. Binary Action Search eliminates the restrictive modification steps of Adaptive Action Modification and requires no temporal action locality in the domain. Our approach is coupled with two well-known reinforcement learning algorithms (Least-Squares Policy Iteration and Fitted Q-Iteration) and its use and properties are thoroughly investigated and demonstrated on the continuous state-action Inverted Pendulum, Double Integrator, and Car on the Hill domains.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {793–800},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553477,
author = {Peters, Jonas and Janzing, Dominik and Gretton, Arthur and Sch\"{o}lkopf, Bernhard},
title = {Detecting the Direction of Causal Time Series},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553477},
doi = {10.1145/1553374.1553477},
abstract = {We propose a method that detects the true direction of time series, by fitting an autoregressive moving average model to the data. Whenever the noise is independent of the previous samples for one ordering of the observations, but dependent for the opposite ordering, we infer the former direction to be the true one. We prove that our method works in the population case as long as the noise of the process is not normally distributed (for the latter case, the direction is not identifiable). A new and important implication of our result is that it confirms a fundamental conjecture in causal reasoning --- if after regression the noise is independent of signal for one direction and dependent for the other, then the former represents the true causal direction --- in the case of time series. We test our approach on two types of data: simulated data sets conforming to our modeling assumptions, and real world EEG time series. Our method makes a decision for a significant fraction of both data sets, and these decisions are mostly correct. For real world data, our approach outperforms alternative solutions to the problem of time direction recovery.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {801–808},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553478,
author = {Petrik, Marek and Zilberstein, Shlomo},
title = {Constraint Relaxation in Approximate Linear Programs},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553478},
abstract = {Approximate Linear Programming (ALP) is a reinforcement learning technique with nice theoretical properties, but it often performs poorly in practice. We identify some reasons for the poor quality of ALP solutions in problems where the approximation induces virtual loops. We then introduce two methods for improving solution quality. One method rolls out selected constraints of the ALP, guided by the dual information. The second method is a relaxation of the ALP, based on external penalty methods. The latter method is applicable in domains in which rolling out constraints is impractical. Both approaches show promising empirical results for simple benchmark problems as well as for a realistic blood inventory management problem.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {809–816},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553479,
author = {Plath, Nils and Toussaint, Marc and Nakajima, Shinichi},
title = {Multi-Class Image Segmentation Using Conditional Random Fields and Global Classification},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553479},
doi = {10.1145/1553374.1553479},
abstract = {A key aspect of semantic image segmentation is to integrate local and global features for the prediction of local segment labels. We present an approach to multi-class segmentation which combines two methods for this integration: a Conditional Random Field (CRF) which couples to local image features and an image classification method which considers global features. The CRF follows the approach of Reynolds & Murphy (2007) and is based on an unsupervised multi scale pre-segmentation of the image into patches, where patch labels correspond to the random variables of the CRF. The output of the classifier is used to constraint this CRF. We demonstrate and compare the approach on a standard semantic segmentation data set.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {817–824},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553480,
author = {P\'{o}czos, Barnab\'{a}s and Abbasi-Yadkori, Yasin and Szepesv\'{a}ri, Csaba and Greiner, Russell and Sturtevant, Nathan},
title = {Learning When to Stop Thinking and Do Something!},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553480},
doi = {10.1145/1553374.1553480},
abstract = {An anytime algorithm is capable of returning a response to the given task at essentially any time; typically the quality of the response improves as the time increases. Here, we consider the challenge of learning when we should terminate such algorithms on each of a sequence of iid tasks, to optimize the expected average reward per unit time. We provide a system for addressing this challenge, which combines the global optimizer Cross-Entropy method with local gradient ascent. This paper theoretically investigates how far the estimated gradient is from the true gradient, then empirically demonstrates that this system is effective by applying it to a toy problem, as well as on a real-world face detection task.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {825–832},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553481,
author = {Putthividhya, Duangmanee (Pew) and Attias, Hagai T. and Nagarajan, Srikantan},
title = {Independent Factor Topic Models},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553481},
doi = {10.1145/1553374.1553481},
abstract = {Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM) have recently emerged as powerful statistical tools for text document modeling. In this paper, we improve upon CTM and propose Independent Factor Topic Models (IFTM) which use linear latent variable models to uncover the hidden sources of correlation between topics. There are 2 main contributions of this work. First, by using a sparse source prior model, we can directly visualize sparse patterns of topic correlations. Secondly, the conditional independence assumption implied in the use of latent source variables allows the objective function to factorize, leading to a fast Newton-Raphson based variational inference algorithm. Experimental results on synthetic and real data show that IFTM runs on average 3--5 times faster than CTM, while giving competitive performance as measured by perplexity and loglikelihood of held-out data.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {833–840},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553482,
author = {Qi, Guo-Jun and Tang, Jinhui and Zha, Zheng-Jun and Chua, Tat-Seng and Zhang, Hong-Jiang},
title = {An Efficient Sparse Metric Learning in High-Dimensional Space via <i>l</i><sub>1</sub>-Penalized Log-Determinant Regularization},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553482},
doi = {10.1145/1553374.1553482},
abstract = {This paper proposes an efficient sparse metric learning algorithm in high dimensional space via an l1-penalized log-determinant regularization. Compare to the most existing distance metric learning algorithms, the proposed algorithm exploits the sparsity nature underlying the intrinsic high dimensional feature space. This sparsity prior of learning distance metric serves to regularize the complexity of the distance model especially in the "less example number p and high dimension d" setting. Theoretically, by analogy to the covariance estimation problem, we find the proposed distance learning algorithm has a consistent result at rate O (√m2 log d)/n) to the target distance matrix with at most m nonzeros per row. Moreover, from the implementation perspective, this l1-penalized log-determinant formulation can be efficiently optimized in a block coordinate descent fashion which is much faster than the standard semi-definite programming which has been widely adopted in many other advanced distance learning algorithms. We compare this algorithm with other state-of-the-art ones on various datasets and competitive results are obtained.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {841–848},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553483,
author = {Qian, Xian and Jiang, Xiaoqian and Zhang, Qi and Huang, Xuanjing and Wu, Lide},
title = {Sparse Higher Order Conditional Random Fields for Improved Sequence Labeling},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553483},
doi = {10.1145/1553374.1553483},
abstract = {In real sequence labeling tasks, statistics of many higher order features are not sufficient due to the training data sparseness, very few of them are useful. We describe Sparse Higher Order Conditional Random Fields (SHO-CRFs), which are able to handle local features and sparse higher order features together using a novel tractable exact inference algorithm. Our main insight is that states and transitions with same potential functions can be grouped together, and inference is performed on the grouped states and transitions. Though the complexity is not polynomial, SHO-CRFs are still efficient in practice because of the feature sparseness. Experimental results on optical character recognition and Chinese organization name recognition show that with the same higher order feature set, SHO-CRFs significantly outperform previous approaches.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {849–856},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553484,
author = {Quattoni, Ariadna and Carreras, Xavier and Collins, Michael and Darrell, Trevor},
title = {An Efficient Projection for <i>l</i><sub>1</sub>, <sub>∞</sub> Regularization},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553484},
doi = {10.1145/1553374.1553484},
abstract = {In recent years the l1, ∞ norm has been proposed for joint regularization. In essence, this type of regularization aims at extending the l1 framework for learning sparse models to a setting where the goal is to learn a set of jointly sparse models. In this paper we derive a simple and effective projected gradient method for optimization of l1, ∞ regularized problems. The main challenge in developing such a method resides on being able to compute efficient projections to the l1, ∞ ball. We present an algorithm that works in O(n log n) time and O(n) memory where n is the number of parameters. We test our algorithm in a multi-task image annotation problem. Our results show that l1, ∞ leads to better performance than both l2 and l1 regularization and that it is is effective in discovering jointly sparse solutions.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {857–864},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553485,
author = {Radovanovi\'{c}, Milo\v{s} and Nanopoulos, Alexandros and Ivanovi\'{c}, Mirjana},
title = {Nearest Neighbors in High-Dimensional Data: The Emergence and Influence of Hubs},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553485},
doi = {10.1145/1553374.1553485},
abstract = {High dimensionality can pose severe difficulties, widely recognized as different aspects of the curse of dimensionality. In this paper we study a new aspect of the curse pertaining to the distribution of k-occurrences, i.e., the number of times a point appears among the k nearest neighbors of other points in a data set. We show that, as dimensionality increases, this distribution becomes considerably skewed and hub points emerge (points with very high k-occurrences). We examine the origin of this phenomenon, showing that it is an inherent property of high-dimensional vector space, and explore its influence on applications based on measuring distances in vector spaces, notably classification, clustering, and information retrieval.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {865–872},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553486,
author = {Raina, Rajat and Madhavan, Anand and Ng, Andrew Y.},
title = {Large-Scale Deep Unsupervised Learning Using Graphics Processors},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553486},
doi = {10.1145/1553374.1553486},
abstract = {The promise of unsupervised learning methods lies in their potential to use vast amounts of unlabeled data to learn complex, highly nonlinear models with millions of free parameters. We consider two well-known unsupervised learning models, deep belief networks (DBNs) and sparse coding, that have recently been applied to a flurry of machine learning applications (Hinton & Salakhutdinov, 2006; Raina et al., 2007). Unfortunately, current learning algorithms for both models are too slow for large-scale applications, forcing researchers to focus on smaller-scale models, or to use fewer training examples.In this paper, we suggest massively parallel methods to help resolve these problems. We argue that modern graphics processors far surpass the computational capabilities of multicore CPUs, and have the potential to revolutionize the applicability of deep unsupervised learning methods. We develop general principles for massively parallelizing unsupervised learning tasks using graphics processors. We show that these principles can be applied to successfully scaling up learning algorithms for both DBNs and sparse coding. Our implementation of DBN learning is up to 70 times faster than a dual-core CPU implementation for large models. For example, we are able to reduce the time required to learn a four-layer DBN with 100 million free parameters from several weeks to around a single day. For sparse coding, we develop a simple, inherently parallel algorithm, that leads to a 5 to 15-fold speedup over previous methods.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {873–880},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553487,
author = {Raman, Sudhir and Fuchs, Thomas J. and Wild, Peter J. and Dahl, Edgar and Roth, Volker},
title = {The Bayesian Group-Lasso for Analyzing Contingency Tables},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553487},
doi = {10.1145/1553374.1553487},
abstract = {Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {881–888},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553488,
author = {Raykar, Vikas C. and Yu, Shipeng and Zhao, Linda H. and Jerebko, Anna and Florin, Charles and Valadez, Gerardo Hermosillo and Bogoni, Luca and Moy, Linda},
title = {Supervised Learning from Multiple Experts: Whom to Trust When Everyone Lies a Bit},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553488},
doi = {10.1145/1553374.1553488},
abstract = {We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {889–896},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553489,
author = {Reid, Mark D. and Williamson, Robert C.},
title = {Surrogate Regret Bounds for Proper Losses},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553489},
doi = {10.1145/1553374.1553489},
abstract = {We present tight surrogate regret bounds for the class of proper (i.e., Fisher consistent) losses. The bounds generalise the margin-based bounds due to Bartlett et al. (2006). The proof uses Taylor's theorem and leads to new representations for loss and regret and a simple proof of the integral representation of proper losses. We also present a different formulation of a duality result of Bregman divergences which leads to a simple demonstration of the convexity of composite losses using canonical link functions.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {897–904},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553490,
author = {Roy, Sushmita and Lane, Terran and Werner-Washburne, Margaret},
title = {Learning Structurally Consistent Undirected Probabilistic Graphical Models},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553490},
doi = {10.1145/1553374.1553490},
abstract = {In many real-world domains, undirected graphical models such as Markov random fields provide a more natural representation of the statistical dependency structure than directed graphical models. Unfortunately, structure learning of undirected graphs using likelihood-based scores remains difficult because of the intractability of computing the partition function. We describe a new Markov random field structure learning algorithm, motivated by canonical parameterization of Abbeel et al. We provide computational improvements on their parameterization by learning per-variable canonical factors, which makes our algorithm suitable for domains with hundreds of nodes. We compare our algorithm against several algorithms for learning undirected and directed models on simulated and real datasets from biology. Our algorithm frequently outperforms existing algorithms, producing higher-quality structures, suggesting that enforcing consistency during structure learning is beneficial for learning undirected graphs.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {905–912},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553491,
author = {Rueping, Stefan},
title = {Ranking Interesting Subgroups},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553491},
doi = {10.1145/1553374.1553491},
abstract = {Subgroup discovery is the task of identifying the top k patterns in a database with most significant deviation in the distribution of a target attribute Y. Subgroup discovery is a popular approach for identifying interesting patterns in data, because it combines statistical significance with an understandable representation of patterns as a logical formula. However, it is often a problem that some subgroups, even if they are statistically highly significant, are not interesting to the user. We present an approach based on the work on ranking Support Vector Machines that ranks subgroups with respect to the user's concept of interestingness, and finds more interesting subgroups. This approach can significantly increase the quality of the subgroups.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {913–920},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553492,
author = {Schmidt, Mikkel N.},
title = {Function Factorization Using Warped Gaussian Processes},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553492},
doi = {10.1145/1553374.1553492},
abstract = {We introduce a new approach to non-linear regression called function factorization, that is suitable for problems where an output variable can reasonably be modeled by a number of multiplicative interaction terms between non-linear functions of the inputs. The idea is to approximate a complicated function on a high-dimensional space by the sum of products of simpler functions on lower-dimensional subspaces. Function factorization can be seen as a generalization of matrix and tensor factorization methods, in which the data are approximated by the sum of outer products of vectors. We present a non-parametric Bayesian approach to function factorization where the priors over the factorizing functions are warped Gaussian processes, and we do inference using Hamiltonian Markov chain Monte Carlo. We demonstrate the superior predictive performance of the method on a food science data set compared to Gaussian process regression and tensor factorization using PARAFAC and GEMANOVA models.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {921–928},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553493,
author = {Shalev-Shwartz, Shai and Tewari, Ambuj},
title = {Stochastic Methods for <i>l</i><sub>1</sub> Regularized Loss Minimization},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553493},
doi = {10.1145/1553374.1553493},
abstract = {We describe and analyze two stochastic methods for l1 regularized loss minimization problems, such as the Lasso. The first method updates the weight of a single feature at each iteration while the second method updates the entire weight vector but only uses a single training example at each iteration. In both methods, the choice of feature/example is uniformly at random. Our theoretical runtime analysis suggests that the stochastic methods should outperform state-of-the-art deterministic approaches, including their deterministic counterparts, when the size of the problem is large. We demonstrate the advantage of stochastic methods by experimenting with synthetic and natural data sets.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {929–936},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553494,
author = {Shaw, Blake and Jebara, Tony},
title = {Structure Preserving Embedding},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553494},
doi = {10.1145/1553374.1553494},
abstract = {Structure Preserving Embedding (SPE) is an algorithm for embedding graphs in Euclidean space such that the embedding is low-dimensional and preserves the global topological properties of the input graph. Topology is preserved if a connectivity algorithm, such as k-nearest neighbors, can easily recover the edges of the input graph from only the coordinates of the nodes after embedding. SPE is formulated as a semidefinite program that learns a low-rank kernel matrix constrained by a set of linear inequalities which captures the connectivity structure of the input graph. Traditional graph embedding algorithms do not preserve structure according to our definition, and thus the resulting visualizations can be misleading or less informative. SPE provides significant improvements in terms of visualization and lossless compression of graphs, outperforming popular methods such as spectral embedding and Laplacian eigen-maps. We find that many classical graphs and networks can be properly embedded using only a few dimensions. Furthermore, introducing structure preserving constraints into dimensionality reduction algorithms produces more accurate representations of high-dimensional data.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {937–944},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553495,
author = {Silver, David and Tesauro, Gerald},
title = {Monte-Carlo Simulation Balancing},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553495},
doi = {10.1145/1553374.1553495},
abstract = {In this paper we introduce the first algorithms for efficiently learning a simulation policy for Monte-Carlo search. Our main idea is to optimise the balance of a simulation policy, so that an accurate spread of simulation outcomes is maintained, rather than optimising the direct strength of the simulation policy. We develop two algorithms for balancing a simulation policy by gradient descent. The first algorithm optimises the balance of complete simulations, using a policy gradient algorithm; whereas the second algorithm optimises the balance over every two steps of simulation. We compare our algorithms to reinforcement learning and supervised learning algorithms for maximising the strength of the simulation policy. We test each algorithm in the domain of 5 x 5 and 6 x 6 Computer Go, using a softmax policy that is parameterised by weights for a hundred simple patterns. When used in a simple Monte-Carlo search, the policies learnt by simulation balancing achieved significantly better performance, with half the mean squared error of a uniform random policy, and similar overall performance to a sophisticated Go engine.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {945–952},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553496,
author = {Sindhwani, Vikas and Melville, Prem and Lawrence, Richard D.},
title = {Uncertainty Sampling and Transductive Experimental Design for Active Dual Supervision},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553496},
doi = {10.1145/1553374.1553496},
abstract = {Dual supervision refers to the general setting of learning from both labeled examples as well as labeled features. Labeled features are naturally available in tasks such as text classification where it is frequently possible to provide domain knowledge in the form of words that associate strongly with a class. In this paper, we consider the novel problem of active dual supervision, or, how to optimally query an example and feature labeling oracle to simultaneously collect two different forms of supervision, with the objective of building the best classifier in the most cost effective manner. We apply classical uncertainty and experimental design based active learning schemes to graph/kernel-based dual supervision models. Empirical studies confirm the potential of these schemes to significantly reduce the cost of acquiring labeled data for training high-quality models.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {953–960},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553497,
author = {Song, Le and Huang, Jonathan and Smola, Alex and Fukumizu, Kenji},
title = {Hilbert Space Embeddings of Conditional Distributions with Applications to Dynamical Systems},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553497},
doi = {10.1145/1553374.1553497},
abstract = {In this paper, we extend the Hilbert space embedding approach to handle conditional distributions. We derive a kernel estimate for the conditional embedding, and show its connection to ordinary embeddings. Conditional embeddings largely extend our ability to manipulate distributions in Hilbert spaces, and as an example, we derive a nonparametric method for modeling dynamical systems where the belief state of the system is maintained as a conditional embedding. Our method is very general in terms of both the domains and the types of distributions that it can handle, and we demonstrate the effectiveness of our method in various dynamical systems. We expect that conditional embeddings will have wider applications beyond modeling dynamical systems.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {961–968},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553498,
author = {Streich, Andreas P. and Frank, Mario and Basin, David and Buhmann, Joachim M.},
title = {Multi-Assignment Clustering for Boolean Data},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553498},
doi = {10.1145/1553374.1553498},
abstract = {Conventional clustering methods typically assume that each data item belongs to a single cluster. This assumption does not hold in general. In order to overcome this limitation, we propose a generative method for clustering vectorial data, where each object can be assigned to multiple clusters. Using a deterministic annealing scheme, our method decomposes the observed data into the contributions of individual clusters and infers their parameters.Experiments on synthetic Boolean data show that our method achieves higher accuracy in the source parameter estimation and superior cluster stability compared to state-of-the-art approaches. We also apply our method to an important problem in computer security known as role mining. Experiments on real-world access control data show performance gains in generalization to new employees against other multi-assignment methods. In challenging situations with high noise levels, our approach maintains its good performance, while alternative state-of-the-art techniques lack robustness.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {969–976},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553499,
author = {Sun, Liang and Ji, Shuiwang and Ye, Jieping},
title = {A Least Squares Formulation for a Class of Generalized Eigenvalue Problems in Machine Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553499},
doi = {10.1145/1553374.1553499},
abstract = {Many machine learning algorithms can be formulated as a generalized eigenvalue problem. One major limitation of such formulation is that the generalized eigenvalue problem is computationally expensive to solve especially for large-scale problems. In this paper, we show that under a mild condition, a class of generalized eigenvalue problems in machine learning can be formulated as a least squares problem. This class of problems include classical techniques such as Canonical Correlation Analysis (CCA), Partial Least Squares (PLS), and Linear Discriminant Analysis (LDA), as well as Hypergraph Spectral Learning (HSL). As a result, various regularization techniques can be readily incorporated into the formulation to improve model sparsity and generalization ability. In addition, the least squares formulation leads to efficient and scalable implementations based on the iterative conjugate gradient type algorithms. We report experimental results that confirm the established equivalence relationship. Results also demonstrate the efficiency and effectiveness of the equivalent least squares formulations on large-scale problems.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {977–984},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553500,
author = {Sutskever, Ilya},
title = {A Simpler Unified Analysis of Budget Perceptrons},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553500},
doi = {10.1145/1553374.1553500},
abstract = {The kernel Perceptron is an appealing online learning algorithm that has a drawback: whenever it makes an error it must increase its support set, which slows training and testing if the number of errors is large. The Forgetron and the Randomized Budget Perceptron algorithms overcome this problem by restricting the number of support vectors the Perceptron is allowed to have. These algorithms have regret bounds whose proofs are dissimilar. In this paper we propose a unified analysis of both of these algorithms by observing that the way in which they remove support vectors can be seen as types of L2-regularization. By casting these algorithms as instances of online convex optimization problems and applying a variant of Zinkevich's theorem for noisy and incorrect gradient, we can bound the regret of these algorithms more easily than before. Our bounds are similar to the existing ones, but the proofs are less technical.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {985–992},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553501,
author = {Sutton, Richard S. and Maei, Hamid Reza and Precup, Doina and Bhatnagar, Shalabh and Silver, David and Szepesv\'{a}ri, Csaba and Wiewiora, Eric},
title = {Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553501},
doi = {10.1145/1553374.1553501},
abstract = {Sutton, Szepesv\'{a}ri and Maei (2009) recently introduced the first temporal-difference learning algorithm compatible with both linear function approximation and off-policy training, and whose complexity scales only linearly in the size of the function approximator. Although their gradient temporal difference (GTD) algorithm converges reliably, it can be very slow compared to conventional linear TD (on on-policy problems where TD is convergent), calling into question its practical utility. In this paper we introduce two new related algorithms with better convergence rates. The first algorithm, GTD2, is derived and proved convergent just as GTD was, but uses a different objective function and converges significantly faster (but still not as fast as conventional TD). The second new algorithm, linear TD with gradient correction, or TDC, uses the same update rule as conventional TD except for an additional term which is initially zero. In our experiments on small test problems and in a Computer Go application with a million features, the learning rate of this algorithm was comparable to that of conventional TD. This algorithm appears to extend linear TD to off-policy learning with no penalty in performance while only doubling computational requirements.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {993–1000},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553502,
author = {Szita, Istv\'{a}n and L\H{o}rincz, Andr\'{a}s},
title = {Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553502},
abstract = {In this paper we propose an algorithm for polynomial-time reinforcement learning in factored Markov decision processes (FMDPs). The factored optimistic initial model (FOIM) algorithm, maintains an empirical model of the FMDP in a conventional way, and always follows a greedy policy with respect to its model. The only trick of the algorithm is that the model is initialized optimistically. We prove that with suitable initialization (i) FOIM converges to the fixed point of approximate value iteration (AVI); (ii) the number of steps when the agent makes non-near-optimal decisions (with respect to the solution of AVI) is polynomial in all relevant quantities; (iii) the per-step costs of the algorithm are also polynomial. To our best knowledge, FOIM is the first algorithm with these properties.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1001–1008},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553503,
author = {Szlam, Arthur and Sapiro, Guillermo},
title = {Discriminative <i>k</i>-Metrics},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553503},
doi = {10.1145/1553374.1553503},
abstract = {The k q-flats algorithm is a generalization of the popular k-means algorithm where q dimensional best fit affine sets replace centroids as the cluster prototypes. In this work, a modification of the k q-flats framework for pattern classification is introduced. The basic idea is to replace the original reconstruction only energy, which is optimized to obtain the k affine spaces, by a new energy that incorporates discriminative terms. This way, the actual classification task is introduced as part of the design and optimization. The presentation of the proposed framework is complemented with experimental results, showing that the method is computationally very efficient and gives excellent results on standard supervised learning benchmarks.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1009–1016},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553504,
author = {Taylor, Gavin and Parr, Ronald},
title = {Kernelized Value Function Approximation for Reinforcement Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553504},
doi = {10.1145/1553374.1553504},
abstract = {A recent surge in research in kernelized approaches to reinforcement learning has sought to bring the benefits of kernelized machine learning techniques to reinforcement learning. Kernelized reinforcement learning techniques are fairly new and different authors have approached the topic with different assumptions and goals. Neither a unifying view nor an understanding of the pros and cons of different approaches has yet emerged. In this paper, we offer a unifying view of the different approaches to kernelized value function approximation for reinforcement learning. We show that, except for different approaches to regularization, Kernelized LSTD (KLSTD) is equivalent to a modelbased approach that uses kernelized regression to find an approximate reward and transition model, and that Gaussian Process Temporal Difference learning (GPTD) returns a mean value function that is equivalent to these other approaches. We also discuss the relationship between our modelbased approach and the earlier Gaussian Processes in Reinforcement Learning (GPRL). Finally, we decompose the Bellman error into the sum of transition error and reward error terms, and demonstrate through experiments that this decomposition can be helpful in choosing regularization parameters.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1017–1024},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553505,
author = {Taylor, Graham W. and Hinton, Geoffrey E.},
title = {Factored Conditional Restricted Boltzmann Machines for Modeling Motion Style},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553505},
doi = {10.1145/1553374.1553505},
abstract = {The Conditional Restricted Boltzmann Machine (CRBM) is a recently proposed model for time series that has a rich, distributed hidden state and permits simple, exact inference. We present a new model, based on the CRBM that preserves its most important computational properties and includes multiplicative three-way interactions that allow the effective interaction weight between two units to be modulated by the dynamic state of a third unit. We factor the three-way weight tensor implied by the multiplicative model, reducing the number of parameters from O(N3) to O(N2). The result is an efficient, compact model whose effectiveness we demonstrate by modeling human motion. Like the CRBM, our model can capture diverse styles of motion with a single set of parameters, and the three-way interactions greatly improve the model's ability to blend motion styles or to transition smoothly among them.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1025–1032},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553506,
author = {Tieleman, Tijmen and Hinton, Geoffrey},
title = {Using Fast Weights to Improve Persistent Contrastive Divergence},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553506},
abstract = {The most commonly used learning algorithm for restricted Boltzmann machines is contrastive divergence which starts a Markov chain at a data point and runs the chain for only a few iterations to get a cheap, low variance estimate of the sufficient statistics under the model. Tieleman (2008) showed that better learning can be achieved by estimating the model's statistics using a small set of persistent "fantasy particles" that are not reinitialized to data points after each weight update. With sufficiently small weight updates, the fantasy particles represent the equilibrium distribution accurately but to explain why the method works with much larger weight updates it is necessary to consider the interaction between the weight updates and the Markov chain. We show that the weight updates force the Markov chain to mix fast, and using this insight we develop an even faster mixing chain that uses an auxiliary set of "fast weights" to implement a temporary overlay on the energy landscape. The fast weights learn rapidly but also decay rapidly and do not contribute to the normal energy landscape that defines the model.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1033–1040},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553507,
author = {Tillman, Robert E.},
title = {Structure Learning with Independent Non-Identically Distributed Data},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553507},
doi = {10.1145/1553374.1553507},
abstract = {There are well known algorithms for learning the structure of directed and undirected graphical models from data, but nearly all assume that the data consists of a single i.i.d. sample. In contexts such as fMRI analysis, data may consist of an ensemble of independent samples from a common data generating mechanism which may not have identical distributions. Pooling such data can result in a number of well known statistical problems so each sample must be analyzed individually, which offers no increase in power due to the presence of multiple samples. We show how existing constraint based methods can be modified to learn structure from the aggregate of such data in a statistically sound manner. The prescribed method is simple to implement and based on existing statistical methods employed in metaanalysis and other areas, but works surprisingly well in this context where there are increased concerns due to issues such as retesting. We report results for directed models, but the method given is just as applicable to undirected models.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1041–1048},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553508,
author = {Toussaint, Marc},
title = {Robot Trajectory Optimization Using Approximate Inference},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553508},
doi = {10.1145/1553374.1553508},
abstract = {The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to first compute an optimal (deterministic) trajectory and then solve a local linear-quadratic-gaussian (LQG) perturbation model to handle the system stochasticity. We present a new algorithm for this approach which improves upon previous algorithms like iLQG. We consider a probabilistic model for which the maximum likelihood (ML) trajectory coincides with the optimal trajectory and which, in the LQG case, reproduces the classical SOC solution. The algorithm then utilizes approximate inference methods (similar to expectation propagation) that efficiently generalize to non-LQG systems. We demonstrate the algorithm on a simulated 39-DoF humanoid robot.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1049–1056},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553509,
author = {Usunier, Nicolas and Buffoni, David and Gallinari, Patrick},
title = {Ranking with Ordered Weighted Pairwise Classification},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553509},
doi = {10.1145/1553374.1553509},
abstract = {In ranking with the pairwise classification approach, the loss associated to a predicted ranked list is the mean of the pairwise classification losses. This loss is inadequate for tasks like information retrieval where we prefer ranked lists with high precision on the top of the list. We propose to optimize a larger class of loss functions for ranking, based on an ordered weighted average (OWA) (Yager, 1988) of the classification losses. Convex OWA aggregation operators range from the max to the mean depending on their weights, and can be used to focus on the top ranked elements as they give more weight to the largest losses. When aggregating hinge losses, the optimization problem is similar to the SVM for interdependent output spaces. Moreover, we show that OWA aggregates of margin-based classification losses have good generalization properties. Experiments on the Letor 3.0 benchmark dataset for information retrieval validate our approach.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1057–1064},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553510,
author = {Varma, Manik and Babu, Bodla Rakesh},
title = {More Generality in Efficient Multiple Kernel Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553510},
doi = {10.1145/1553374.1553510},
abstract = {Recent advances in Multiple Kernel Learning (MKL) have positioned it as an attractive tool for tackling many supervised learning tasks. The development of efficient gradient descent based optimization schemes has made it possible to tackle large scale problems. Simultaneously, MKL based algorithms have achieved very good results on challenging real world applications. Yet, despite their successes, MKL approaches are limited in that they focus on learning a linear combination of given base kernels.In this paper, we observe that existing MKL formulations can be extended to learn general kernel combinations subject to general regularization. This can be achieved while retaining all the efficiency of existing large scale optimization algorithms. To highlight the advantages of generalized kernel learning, we tackle feature selection problems on benchmark vision and UCI databases. It is demonstrated that the proposed formulation can lead to better results not only as compared to traditional MKL but also as compared to state-of-the-art wrapper and filter methods for feature selection.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1065–1072},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553511,
author = {Vinh, Nguyen Xuan and Epps, Julien and Bailey, James},
title = {Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary?},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553511},
doi = {10.1145/1553374.1553511},
abstract = {Information theoretic based measures form a fundamental class of similarity measures for comparing clusterings, beside the class of pair-counting based and set-matching based measures. In this paper, we discuss the necessity of correction for chance for information theoretic based measures for clusterings comparison. We observe that the baseline for such measures, i.e. average value between random partitions of a data set, does not take on a constant value, and tends to have larger variation when the ratio between the number of data points and the number of clusters is small. This effect is similar in some other non-information theoretic based measures such as the well-known Rand Index. Assuming a hypergeometric model of randomness, we derive the analytical formula for the expected mutual information value between a pair of clusterings, and then propose the adjusted version for several popular information theoretic based measures. Some examples are given to demonstrate the need and usefulness of the adjusted measures.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1073–1080},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553512,
author = {Vlassis, Nikos and Toussaint, Marc},
title = {Model-Free Reinforcement Learning as Mixture Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553512},
doi = {10.1145/1553374.1553512},
abstract = {We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizon cases. We describe a Stochastic Approximation EM algorithm for likelihood maximization that, in the tabular case, is equivalent to a non-bootstrapping optimistic policy iteration algorithm like Sarsa(1) that can be applied both in MDPs and POMDPs. On the theoretical side, by relating the proposed stochastic EM algorithm to the family of optimistic policy iteration algorithms, we provide new tools that permit the design and analysis of algorithms in that family. On the practical side, preliminary experiments on a POMDP problem demonstrated encouraging results.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1081–1088},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553513,
author = {Volkovs, Maksims N. and Zemel, Richard S.},
title = {BoltzRank: Learning to Maximize Expected Ranking Gain},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553513},
abstract = {Ranking a set of retrieved documents according to their relevance to a query is a popular problem in information retrieval. Methods that learn ranking functions are difficult to optimize, as ranking performance is typically judged by metrics that are not smooth. In this paper we propose a new listwise approach to learning to rank. Our method creates a conditional probability distribution over rankings assigned to documents for a given query, which permits gradient ascent optimization of the expected value of some performance measure. The rank probabilities take the form of a Boltzmann distribution, based on an energy function that depends on a scoring function composed of individual and pairwise potentials. Including pairwise potentials is a novel contribution, allowing the model to encode regularities in the relative scores of documents; existing models assign scores at test time based only on individual documents, with no pairwise constraints between documents. Experimental results on the LETOR3.0 data set show that our method out-performs existing learning approaches to ranking.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1089–1096},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553514,
author = {Wagstaff, Kiri L. and Bornstein, Benjamin},
title = {K-Means in Space: A Radiation Sensitivity Evaluation},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553514},
doi = {10.1145/1553374.1553514},
abstract = {Spacecraft increasingly employ onboard data analysis to inform further data collection and prioritization decisions. However, many spacecraft operate in high-radiation environments in which the reliability of dataintensive computation is not known. This paper presents the first study of radiation sensitivity for k-means clustering. Our key findings are 1) k-means data structures differ in sensitivity, which is not determined solely by the amount of memory exposed; 2) no special radiation protection is needed below a data-set-dependent radiation threshold, enabling the use of faster, smaller, and cheaper onboard memory; and 3) subsampling improves radiation tolerance slightly, but the use of kd-trees unfortunately reduces tolerance. Our conclusions can help tailor k-means for use in future high-radiation environments.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1097–1104},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553515,
author = {Wallach, Hanna M. and Murray, Iain and Salakhutdinov, Ruslan and Mimno, David},
title = {Evaluation Methods for Topic Models},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553515},
abstract = {A natural evaluation metric for statistical topic models is the probability of held-out documents given a trained model. While exact computation of this probability is intractable, several estimators for this probability have been used in the topic modeling literature, including the harmonic mean method and empirical likelihood method. In this paper, we demonstrate experimentally that commonly-used methods are unlikely to accurately estimate the probability of held-out documents, and propose two alternative methods that are both accurate and efficient.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1105–1112},
numpages = {8}
}

@inbook{10.1145/1553374.1553516,
author = {Weinberger, Kilian and Dasgupta, Anirban and Langford, John and Smola, Alex and Attenberg, Josh},
title = {Feature Hashing for Large Scale Multitask Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553516},
abstract = {Empirical evidence suggests that hashing is an effective strategy for dimensionality reduction and practical nonparametric estimation. In this paper we provide exponential tail bounds for feature hashing and show that the interaction between random subspaces is negligible with high probability. We demonstrate the feasibility of this approach with experimental results for a new use case --- multitask learning with hundreds of thousands of tasks.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1113–1120},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553517,
author = {Welling, Max},
title = {Herding Dynamical Weights to Learn},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553517},
doi = {10.1145/1553374.1553517},
abstract = {A new "herding" algorithm is proposed which directly converts observed moments into a sequence of pseudo-samples. The pseudo-samples respect the moment constraints and may be used to estimate (unobserved) quantities of interest. The procedure allows us to sidestep the usual approach of first learning a joint model (which is intractable) and then sampling from that model (which can easily get stuck in a local mode). Moreover, the algorithm is fully deterministic, avoiding random number generation) and does not need expensive operations such as exponentiation.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1121–1128},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553518,
author = {Wood, Frank and Archambeau, C\'{e}dric and Gasthaus, Jan and James, Lancelot and Teh, Yee Whye},
title = {A Stochastic Memoizer for Sequence Data},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553518},
abstract = {We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares statistical strength between subsequent symbol predictive distributions in such a way that predictive performance generalizes well. The model builds on a specific parameterization of an unbounded-depth hierarchical Pitman-Yor process. We introduce analytic marginalization steps (using coagulation operators) to reduce this model to one that can be represented in time and space linear in the length of the training sequence. We show how to perform inference in such a model without truncation approximation and introduce fragmentation operators necessary to do predictive inference. We demonstrate the sequence memoizer by using it as a language model, achieving state-of-the-art results.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1129–1136},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553519,
author = {Xu, Linli and White, Martha and Schuurmans, Dale},
title = {Optimal Reverse Prediction: A Unified Perspective on Supervised, Unsupervised and Semi-Supervised Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553519},
doi = {10.1145/1553374.1553519},
abstract = {Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unification of several supervised and unsupervised training principles through the concept of optimal reverse prediction: predict the inputs from the target labels, optimizing both over model parameters and any missing labels. In particular, we show how supervised least squares, principal components analysis, k-means clustering and normalized graph-cut can all be expressed as instances of the same training principle. Natural forms of semi-supervised regression and classification are then automatically derived, yielding semi-supervised learning algorithms for regression and classification that, surprisingly, are novel and refine the state of the art. These algorithms can all be combined with standard regularizers and made non-linear via kernels.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1137–1144},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553520,
author = {Xu, Zenglin and Jin, Rong and Ye, Jieping and Lyu, Michael R. and King, Irwin},
title = {Non-Monotonic Feature Selection},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553520},
doi = {10.1145/1553374.1553520},
abstract = {We consider the problem of selecting a subset of m most informative features where m is the number of required features. This feature selection problem is essentially a combinatorial optimization problem, and is usually solved by an approximation. Conventional feature selection methods address the computational challenge in two steps: (a) ranking all the features by certain scores that are usually computed independently from the number of specified features m, and (b) selecting the top m ranked features. One major shortcoming of these approaches is that if a feature f is chosen when the number of specified features is m, it will always be chosen when the number of specified features is larger than m. We refer to this property as the "monotonic" property of feature selection. In this work, we argue that it is important to develop efficient algorithms for non-monotonic feature selection. To this end, we develop an algorithm for non-monotonic feature selection that approximates the related combinatorial optimization problem by a Multiple Kernel Learning (MKL) problem. We also present a strategy that derives a discrete solution from the approximate solution of MKL, and show the performance guarantee for the derived discrete solution when compared to the global optimal solution for the related combinatorial optimization problem. An empirical study with a number of benchmark data sets indicates the promising performance of the proposed framework compared with several state-of-the-art approaches for feature selection.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1145–1152},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553521,
author = {Yang, Liu and Jin, Rong and Ye, Jieping},
title = {Online Learning by Ellipsoid Method},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553521},
abstract = {In this work, we extend the ellipsoid method, which was originally designed for convex optimization, for online learning. The key idea is to approximate by an ellipsoid the classification hypotheses that are consistent with all the training examples received so far. This is in contrast to most online learning algorithms where only a single classifier is maintained at each iteration. Efficient algorithms are presented for updating both the centroid and the positive definite matrix of ellipsoid given a misclassified example. In addition to the classical ellipsoid method, an improved version for online learning is also presented. Mistake bounds for both ellipsoid methods are derived. Evaluation with the USPS dataset and three UCI data-sets shows encouraging results when comparing the proposed online learning algorithm to two state-of-the-art online learners.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1153–1160},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553522,
author = {Yi, Sun and Wierstra, Daan and Schaul, Tom and Schmidhuber, J\"{u}rgen},
title = {Stochastic Search Using the Natural Gradient},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553522},
doi = {10.1145/1553374.1553522},
abstract = {To optimize unknown 'fitness' functions, we present Natural Evolution Strategies, a novel algorithm that constitutes a principled alternative to standard stochastic search methods. It maintains a multinormal distribution on the set of solution candidates. The Natural Gradient is used to update the distribution's parameters in the direction of higher expected fitness, by efficiently calculating the inverse of the exact Fisher information matrix whereas previous methods had to use approximations. Other novel aspects of our method include optimal fitness baselines and importance mixing, a procedure adjusting batches with minimal numbers of fitness evaluations. The algorithm yields competitive results on a number of benchmarks.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1161–1168},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553523,
author = {Yu, Chun-Nam John and Joachims, Thorsten},
title = {Learning Structural SVMs with Latent Variables},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553523},
doi = {10.1145/1553374.1553523},
abstract = {We present a large-margin formulation and algorithm for structured output prediction that allows the use of latent variables. Our proposal covers a large range of application problems, with an optimization problem that can be solved efficiently using Concave-Convex Programming. The generality and performance of the approach is demonstrated through three applications including motiffinding, noun-phrase coreference resolution, and optimizing precision at k in information retrieval.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1169–1176},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553524,
author = {Yu, Jia Yuan and Mannor, Shie},
title = {Piecewise-Stationary Bandit Problems with Side Observations},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553524},
doi = {10.1145/1553374.1553524},
abstract = {We consider a sequential decision problem where the rewards are generated by a piecewise-stationary distribution. However, the different reward distributions are unknown and may change at unknown instants. Our approach uses a limited number of side observations on past rewards, but does not require prior knowledge of the frequency of changes. In spite of the adversarial nature of the reward process, we provide an algorithm whose regret, with respect to the baseline with perfect knowledge of the distributions and the changes, is O(k log(T)), where k is the number of changes up to time T. This is in contrast to the case where side observations are not available, and where the regret is at least Ω(√T).},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1177–1184},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553525,
author = {Yu, Kai and Lafferty, John and Zhu, Shenghuo and Gong, Yihong},
title = {Large-Scale Collaborative Prediction Using a Nonparametric Random Effects Model},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553525},
abstract = {A nonparametric model is introduced that allows multiple related regression tasks to take inputs from a common data space. Traditional transfer learning models can be inappropriate if the dependence among the outputs cannot be fully resolved by known input-specific and task-specific predictors. The proposed model treats such output responses as conditionally independent, given known predictors and appropriate unobserved random effects. The model is nonparametric in the sense that the dimensionality of random effects is not specified a priori but is instead determined from data. An approach to estimating the model is presented uses an EM algorithm that is efficient on a very large scale collaborative prediction problem. The obtained prediction accuracy is competitive with state-of-the-art results.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1185–1192},
numpages = {8}
}

@inbook{10.1145/1553374.1553526,
author = {Yuan, Xiao-Tong and Hu, Bao-Gang},
title = {Robust Feature Extraction via Information Theoretic Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553526},
abstract = {In this paper, we present a robust feature extraction framework based on information-theoretic learning. Its formulated objective aims at simultaneously maximizing the Renyi's quadratic information potential of features and the Renyi's cross information potential between features and class labels. This objective function reaps the advantages in robustness from both redescending M-estimator and manifold regularization, and can be efficiently optimized via half-quadratic optimization in an iterative manner. In addition, the popular algorithms LPP, SRDA and LapRLS for feature extraction are all justified to be the special cases within this framework. Extensive comparison experiments on several real-world data sets, with contaminated features or labels, well validate the encouraging gain in algorithmic robustness from this proposed framework.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1193–1200},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553527,
author = {Yue, Yisong and Joachims, Thorsten},
title = {Interactively Optimizing Information Retrieval Systems as a Dueling Bandits Problem},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553527},
doi = {10.1145/1553374.1553527},
abstract = {We present an on-line learning framework tailored towards real-time learning from observed user behavior in search engines and other information retrieval systems. In particular, we only require pairwise comparisons which were shown to be reliably inferred from implicit feedback (Joachims et al., 2007; Radlinski et al., 2008b). We will present an algorithm with theoretical guarantees as well as simulation results.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1201–1208},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553528,
author = {Yuille, Alan and Zheng, Songfeng},
title = {Compositional Noisy-Logical Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553528},
doi = {10.1145/1553374.1553528},
abstract = {We describe a new method for learning the conditional probability distribution of a binary-valued variable from labelled training examples. Our proposed Compositional Noisy-Logical Learning (CNLL) approach learns a noisy-logical distribution in a compositional manner. CNLL is an alternative to the well-known AdaBoost algorithm which performs coordinate descent on an alternative error measure. We describe two CNLL algorithms and test their performance compared to AdaBoost on two types of problem: (i) noisy-logical data (such as noisy exclusive-or), and (ii) four standard datasets from the UCI repository. Our results show that we outperform AdaBoost while using significantly fewer weak classifiers, thereby giving a more transparent classifier suitable for knowledge extraction.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1209–1216},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553529,
author = {Zang, Peng and Zhou, Peng and Minnen, David and Isbell, Charles},
title = {Discovering Options from Example Trajectories},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553529},
doi = {10.1145/1553374.1553529},
abstract = {We present a novel technique for automated problem decomposition to address the problem of scalability in reinforcement learning. Our technique makes use of a set of near-optimal trajectories to discover options and incorporates them into the learning process, dramatically reducing the time it takes to solve the underlying problem. We run a series of experiments in two different domains and show that our method offers up to 30 fold speedup over the baseline.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1217–1224},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553530,
author = {Zhan, De-Chuan and Li, Ming and Li, Yu-Feng and Zhou, Zhi-Hua},
title = {Learning Instance Specific Distances Using Metric Propagation},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553530},
doi = {10.1145/1553374.1553530},
abstract = {In many real-world applications, such as image retrieval, it would be natural to measure the distances from one instance to others using instance specific distance which captures the distinctions from the perspective of the concerned instance. However, there is no complete framework for learning instance specific distances since existing methods are incapable of learning such distances for test instance and unlabeled data. In this paper, we propose the Isd method to address this issue. The key of Isd is metric propagation, that is, propagating and adapting metrics of individual labeled examples to individual unlabeled instances. We formulate the problem into a convex optimization framework and derive efficient solutions. Experiments show that Isd can effectively learn instance specific distances for labeled as well as unlabeled instances. The metric propagation scheme can also be used in other scenarios.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1225–1232},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553531,
author = {Zhang, Kai and Kwok, James T. and Parvin, Bahram},
title = {Prototype Vector Machine for Large Scale Semi-Supervised Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553531},
abstract = {Practical data mining rarely falls exactly into the supervised learning scenario. Rather, the growing amount of unlabeled data poses a big challenge to large-scale semi-supervised learning (SSL). We note that the computational intensiveness of graph-based SSL arises largely from the manifold or graph regularization, which in turn lead to large models that are difficult to handle. To alleviate this, we proposed the prototype vector machine (PVM), a highly scalable, graph-based algorithm for large-scale SSL. Our key innovation is the use of "prototypes vectors" for efficient approximation on both the graph-based regularizer and model representation. The choice of prototypes are grounded upon two important criteria: they not only perform effective low-rank approximation of the kernel matrix, but also span a model suffering the minimum information loss compared with the complete model. We demonstrate encouraging performance and appealing scaling properties of the PVM on a number of machine learning benchmark data sets.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1233–1240},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553533,
author = {Zhang, Wei and Surve, Akshat and Fern, Xiaoli and Dietterich, Thomas},
title = {Learning Non-Redundant Codebooks for Classifying Complex Objects},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553533},
doi = {10.1145/1553374.1553533},
abstract = {Codebook-based representations are widely employed in the classification of complex objects such as images and documents. Most previous codebook-based methods construct a single codebook via clustering that maps a bag of low-level features into a fixed-length histogram that describes the distribution of these features. This paper describes a simple yet effective framework for learning multiple non-redundant codebooks that produces surprisingly good results. In this framework, each codebook is learned in sequence to extract discriminative information that was not captured by preceding codebooks and their corresponding classifiers. We apply this framework to two application domains: visual object categorization and document classification. Experiments on large classification tasks show substantial improvements in performance compared to a single codebook or codebooks learned in a bagging style.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1241–1248},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553534,
author = {Zhou, Zhi-Hua and Sun, Yu-Yin and Li, Yu-Feng},
title = {Multi-Instance Learning by Treating Instances as Non-I.I.D. Samples},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553534},
doi = {10.1145/1553374.1553534},
abstract = {Previous studies on multi-instance learning typically treated instances in the bags as independently and identically distributed. The instances in a bag, however, are rarely independent in real tasks, and a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits relations among instances. In this paper, we propose two simple yet effective methods. In the first method, we explicitly map every bag to an undirected graph and design a graph kernel for distinguishing the positive and negative bags. In the second method, we implicitly construct graphs by deriving affinity matrices and propose an efficient graph kernel considering the clique information. The effectiveness of the proposed methods are validated by experiments.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1249–1256},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553535,
author = {Zhu, Jun and Ahmed, Amr and Xing, Eric P.},
title = {MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553535},
doi = {10.1145/1553374.1553535},
abstract = {Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents; and existing models apply likelihood-based estimation. In this paper, we present a max-margin supervised topic model for both continuous and categorical response variables. Our approach, the maximum entropy discrimination latent Dirichlet allocation (MedLDA), utilizes the max-margin principle to train supervised topic models and estimate predictive topic representations that are arguably more suitable for prediction. We develop efficient variational methods for posterior inference and demonstrate qualitatively and quantitatively the advantages of MedLDA over likelihood-based topic models on movie review and 20 Newsgroups data sets.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1257–1264},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553536,
author = {Zhu, Jun and Xing, Eric P.},
title = {On Primal and Dual Sparsity of Markov Networks},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553536},
doi = {10.1145/1553374.1553536},
abstract = {Sparsity is a desirable property in high dimensional learning. The l1-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity. Combining these two methods, an l1-norm max-margin Markov network (l1-M3N) can achieve both types of sparsity. This paper analyzes its connections to the Laplace max-margin Markov network (LapM3N), which inherits the dual sparsity of max-margin models but is pseudo-primal sparse, and to a novel adaptive M3N (AdapM3N). We show that the l1-M3N is an extreme case of the LapM3N, and the l1-M3N is equivalent to an AdapM3N. Based on this equivalence we develop a robust EM-style algorithm for learning an l1-M3N. We demonstrate the advantages of the simultaneously (pseudo-) primal and dual sparse models over the ones which enjoy either primal or dual sparsity on both synthetic and real data sets.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1265–1272},
numpages = {8},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553537,
author = {Zhuang, Jinfeng and Tsang, Ivor W. and Hoi, Steven C. H.},
title = {SimpleNPKL: Simple Non-Parametric Kernel Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553537},
abstract = {Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time complexity of standard interior-point SDP solvers could be as high as O(n6.5). Such intensive computation cost prohibits NPK learning applicable to real applications, even for data sets of moderate size. In this paper, we propose an efficient approach to NPK learning from side information, referred to as SimpleNPKL, which can efficiently learn non-parametric kernels from large sets of pairwise constraints. In particular, we show that the proposed SimpleNPKL with linear loss has a closed-form solution that can be simply computed by the Lanczos algorithm. Moreover, we show that the SimpleNPKL with square hinge loss can be re-formulated as a saddle-point optimization task, which can be further solved by a fast iterative algorithm. In contrast to the previous approaches, our empirical results show that our new technique achieves the same accuracy, but is significantly more efficient and scalable.},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
pages = {1273–1280},
numpages = {8}
}

@inproceedings{10.1145/1553374.1553538,
author = {Cortes, Corinna},
title = {Invited Talk: Can Learning Kernels Help Performance?},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553538},
doi = {10.1145/1553374.1553538},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {1},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553539,
author = {Freund, Yoav},
title = {Invited Talk: Drifting Games, Boosting and Online Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553539},
doi = {10.1145/1553374.1553539},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {2},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553540,
author = {Agosta, John Mark and Almond, Russell and Buede, Dennis and Druzdzel, Marek J. and Goldsmith, Judy and Renooij, Silja},
title = {Workshop Summary: Seventh Annual Workshop on Bayes Applications},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553540},
doi = {10.1145/1553374.1553540},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {3},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553542,
author = {Murphy, Robert F. and Hsu, Chun-Nan and Nanni, Loris},
title = {Workshop Summary: Automated Interpretation and Modelling of Cell Images},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553542},
doi = {10.1145/1553374.1553542},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {4},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553543,
author = {Yu, Kay and Salakhutdinov, Ruslan and LeCun, Yann and Hinton, Geoff and Bengio, Yoshua},
title = {Workshop Summary: Workshop on Learning Feature Hierarchies},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553543},
doi = {10.1145/1553374.1553543},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {5},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553544,
author = {Wingate, David and Diuk, Carlos and Li, Lihong and Taylor, Matthew and Frank, Jordan},
title = {Workshop Summary: Results of the 2009 Reinforcement Learning Competition},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553544},
doi = {10.1145/1553374.1553544},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {6},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553546,
author = {Drummond, Chris and Japkowicz, Nathalie and Klement, William and Macskassy, Sofus},
title = {Workshop Summary: The Fourth Workshop on Evaluation Methods for Machine Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553546},
doi = {10.1145/1553374.1553546},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {7},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553547,
author = {Audibert, Jean-Yves and Auer, Peter and Lazaric, Alessandro and Munos, Remi and Ryabko, Daniil and Szepesvari, Csaba},
title = {Workshop Summary: On-Line Learning with Limited Feedback},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553547},
doi = {10.1145/1553374.1553547},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {8},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553548,
author = {Seeger, Matthias and Sra, Suvrit and Cunningham, John P.},
title = {Workshop Summary: Numerical Mathematics in Machine Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553548},
doi = {10.1145/1553374.1553548},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {9},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553550,
author = {Simsek, Ozgur},
title = {Workshop Summary: Abstraction in Reinforcement Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553550},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {10},
numpages = {1}
}

@inproceedings{10.1145/1553374.1553551,
author = {Eck, Douglas and Ellis, Dan and Hamel, Philippe},
title = {Workshop Summary: Sparse Methods for Music Audio},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553551},
doi = {10.1145/1553374.1553551},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {11},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553552,
author = {Beygelzimer, Alina and Langford, John and Zadrozny, Bianca},
title = {Tutorial Summary: Reductions in Machine Learning},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553552},
doi = {10.1145/1553374.1553552},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {12},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inbook{10.1145/1553374.1553553,
author = {Even-Dar, Eyal and Mirrokni, Vahab},
title = {Tutorial Summary: Convergence of Natural Dynamics to Equilibria},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553553},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {13},
numpages = {1}
}

@inproceedings{10.1145/1553374.1553554,
author = {Tresp, Volker and Yu, Kai},
title = {Tutorial Summary: Learning with Dependencies between Several Response Variables},
year = {2009},
isbn = {9781605585161},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/1553374.1553554},
doi = {10.1145/1553374.1553554},
booktitle = {Proceedings of the 26th Annual International Conference on Machine Learning},
articleno = {14},
numpages = {1},
location = {Montreal, Quebec, Canada},
series = {ICML '09}
}

@inproceedings{10.1145/1553374.1553555,
author = {Warmuth, Manfred K. and Vishwanathan, S.V.N.},
title = {Tutorial Summary: Survey of Boosting from an Optimization Perspective},
year = {2009},
isbn = {978160

license_type: permissive

branch_name: refs/heads/main

extension: bib