text:
@inproceedings{10.1145/2623330.2630816,
author = {Etzioni, Oren},
title = {The Battle for the Future of Data Mining},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630816},
doi = {10.1145/2623330.2630816},
abstract = {Deep learning has catapulted to the front page of the New York Times, formed the core of the so-called 'Google brain', and achieved impressive results in vision, speech recognition, and elsewhere. Yet researchers have offered simple conundrums that deep learning doesn't address. For example, consider the sentence: 'The large ball crashed right through the table because it was made of Styrofoam.' What was made of Styrofoam? The large ball? Or the table? The answer is obviously 'the table', but if we change the word 'Styrofoam' to 'steel', the answer is clearly 'the large ball'. To automatically answer this type of question, our computers require an extensive body of knowledge. We believe that text mining can provide the requisite body of knowledge. My talk will describe work at the new Allen Institute for AI towards building the next-generation of text-mining systems.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {1},
numpages = {1},
keywords = {text-mining, data mining, deep learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630815,
author = {Horvitz, Eric},
title = {Data, Predictions, and Decisions in Support of People and Society},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630815},
doi = {10.1145/2623330.2630815},
abstract = {Deep societal benefits will spring from advances in data availability and in computational procedures for mining insights and inferences from large data sets. I will describe efforts to harness data for making predictions and guiding decisions, touching on work in transportation, healthcare, online services, and interactive systems. I will start with efforts to learn and field predictive models that forecast flows of traffic in greater city regions. Moving from the ground to the air, I will discuss fusing data from aircraft to make inferences about atmospheric conditions and using these results to enhance air transport. I will then focus on experiences with building and fielding predictive models in clinical medicine. I will show how inferences about outcomes and interventions can provide insights and guide decision making. Moving beyond data captured by hospitals, I will discuss the promise of transforming anonymized behavioral data drawn from web services into large-scale sensor networks for public health, including efforts to identify adverse effects of medications and to understand illness in populations. I will conclude by describing how we can use machine learning to leverage the complementarity of human and machine intellect to solve challenging problems in science and society.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {2},
numpages = {1},
keywords = {probabilistic graphical models, machine learning, transportation, probabilistic inference, healthcare, decision analysis},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630817,
author = {Schadt, Eric},
title = {A Data Driven Approach to Diagnosing and Treating Disease},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630817},
doi = {10.1145/2623330.2630817},
abstract = {Throughout the biomedical and life sciences research community, advanced integrative biology algorithms are employed to integrate large scale data across many different high-dimensional datatypes to construct predictive network models of disease. The causal inference approaches we employ for this purpose well complement the types of natural artificial intelligence/machine learning approaches that have become nearly standard in the life and biomedical sciences for building classifiers for a range of problems, from disease classification and subtype stratification, to the identification of responders and non-responders for a given treatment strategy. By building a causal network model that spans multiple scales (from the molecular to the cellular, to the tissue/organ, to the organism and community) we can understand the flow of information and how best to modulate that flow to improve human wellbeing, whether better diagnosing and treating disease or improving overall health(1-4). More specifically, we have constructed predictive network models for Alzheimer's disease, along with other common human diseases such as obesity, diabetes, heart disease, and inflammatory bowel disease, and cancer, and demonstrated a causal network common across all of these diseases(3, 5-10). Not only do we demonstrate that our predictive models uncover important mechanisms of disease and mechanistic connections among different diseases, but that they have led to a natural way to prioritize therapeutic points of intervention and provide optimal molecular phenotypes for high throughput screening. Our application of these models in a number of disease areas has led to the identification of novel genes that are causal for disease and that may serve as efficacious points of therapeutic intervention, as well as to personalized treatment strategies that provide a more quantitative and accurate approach to tailoring treatments to specific forms of disease.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {3},
numpages = {1},
keywords = {multiscale biology, causal network inference, predictive network modeling, drug discovery, systems biology},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2630818,
author = {Mullainathan, Sendhil},
title = {Bugbears or Legitimate Threats? (Social) Scientists' Criticisms of Machine Learning?},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2630818},
doi = {10.1145/2623330.2630818},
abstract = {Social scientists increasingly criticize the use of machine learning techniques to understand human behavior. Criticisms include: (1) They are atheoretical and hence of limited scientific value; (2) They do not address causality and are hence of limited policy value; and (3) They are uninterpretable and hence of limited generalizability value (outside contexts very narrowly similar to the training dataset). These criticisms, I argue, miss the enormous opportunity offered by ML techniques to fundamentally improve the practice of empirical social science. Yet each criticism does contain a grain of truth and overcoming them will require innovations to existing methodologies. Some of these innovations are being developed today and some are yet to be tackled. I will in this talk sketch (1) what these innovations look like or should look like; (2) why they are needed; and (3) the technical challenges they raise. I will illustrate my points using a set of applications that range from financial markets to social policy problems to computational models of basic psychological processes. This talk describes joint work with Jon Kleinberg and individual projects with Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Anuj Shah, Chenhao Tan, Mike Yeomans and Tom Zimmerman.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {4},
numpages = {1},
keywords = {n/a},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623628,
author = {Song, Xuan and Zhang, Quanshi and Sekimoto, Yoshihide and Shibasaki, Ryosuke},
title = {Prediction of Human Emergency Behavior and Their Mobility Following Large-Scale Disaster},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623628},
doi = {10.1145/2623330.2623628},
abstract = {The frequency and intensity of natural disasters has significantly increased over the past decades and this trend is predicted to continue. Facing these possible and unexpected disasters, accurately predicting human emergency behavior and their mobility will become the critical issue for planning effective humanitarian relief, disaster management, and long-term societal reconstruction. In this paper, we build up a large human mobility database (GPS records of 1.6 million users over one year) and several different datasets to capture and analyze human emergency behavior and their mobility following the Great East Japan Earthquake and Fukushima nuclear accident. Based on our empirical analysis through these data, we find that human behavior and their mobility following large-scale disaster sometimes correlate with their mobility patterns during normal times, and are also highly impacted by their social relationship, intensity of disaster, damage level, government appointed shelters, news reporting, large population flow and etc. On the basis of these findings, we develop a model of human behavior that takes into account these factors for accurately predicting human emergency behavior and their mobility following large-scale disaster. The experimental results and validations demonstrate the efficiency of our behavior model, and suggest that human behavior and their movements during disasters may be significantly more predictable than previously thought.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {5–14},
numpages = {10},
keywords = {human mobility, spatio-temporal data mining, disaster informatics},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623703,
author = {Dong, Yuxiao and Yang, Yang and Tang, Jie and Yang, Yang and Chawla, Nitesh V.},
title = {Inferring User Demographics and Social Strategies in Mobile Social Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623703},
doi = {10.1145/2623330.2623703},
abstract = {Demographics are widely used in marketing to characterize different types of customers. However, in practice, demographic information such as age, gender, and location is usually unavailable due to privacy and other reasons. In this paper, we aim to harness the power of big data to automatically infer users' demographics based on their daily mobile communication patterns. Our study is based on a real-world large mobile network of more than 7,000,000 users and over 1,000,000,000 communication records (CALL and SMS). We discover several interesting social strategies that mobile users frequently use to maintain their social connections. First, young people are very active in broadening their social circles, while seniors tend to keep close but more stable connections. Second, female users put more attention on cross-generation interactions than male users, though interactions between male and female users are frequent. Third, a persistent same-gender triadic pattern over one's lifetime is discovered for the first time, while more complex opposite-gender triadic patterns are only exhibited among young people.We further study to what extent users' demographics can be inferred from their mobile communications. As a special case, we formalize a problem of double dependent-variable prediction-inferring user gender and age simultaneously. We propose the WhoAmI method, a Double Dependent-Variable Factor Graph Model, to address this problem by considering not only the effects of features on gender/age, but also the interrelation between gender and age. Our experiments show that the proposed WhoAmI method significantly improves the prediction accuracy by up to 10% compared with several alternative methods.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {15–24},
numpages = {10},
keywords = {social strategy, human communication, mobile social network, demographic prediction},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623656,
author = {Wang, Yilun and Zheng, Yu and Xue, Yexiang},
title = {Travel Time Estimation of a Path Using Sparse Trajectories},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623656},
doi = {10.1145/2623330.2623656},
abstract = {In this paper, we propose a citywide and real-time model for estimating the travel time of any path (represented as a sequence of connected road segments) in real time in a city, based on the GPS trajectories of vehicles received in current time slots and over a period of history as well as map data sources. Though this is a strategically important task in many traffic monitoring and routing systems, the problem has not been well solved yet given the following three challenges. The first is the data sparsity problem, i.e., many road segments may not be traveled by any GPS-equipped vehicles in present time slot. In most cases, we cannot find a trajectory exactly traversing a query path either. Second, for the fragment of a path with trajectories, they are multiple ways of using (or combining) the trajectories to estimate the corresponding travel time. Finding an optimal combination is a challenging problem, subject to a tradeoff between the length of a path and the number of trajectories traversing the path (i.e., support). Third, we need to instantly answer users' queries which may occur in any part of a given city. This calls for an efficient, scalable and effective solution that can enable a citywide and real-time travel time estimation. To address these challenges, we model different drivers' travel times on different road segments in different time slots with a three dimension tensor. Combined with geospatial, temporal and historical contexts learned from trajectories and map data, we fill in the tensor's missing values through a context-aware tensor decomposition approach. We then devise and prove an object function to model the aforementioned tradeoff, with which we find the most optimal concatenation of trajectories for an estimate through a dynamic programming solution. In addition, we propose using frequent trajectory patterns (mined from historical trajectories) to scale down the candidates of concatenation and a suffix-tree-based index to manage the trajectories received in the present time slot. We evaluate our method based on extensive experiments, using GPS trajectories generated by more than 32,000 taxis over a period of two months. The results demonstrate the effectiveness, efficiency and scalability of our method beyond baseline approaches.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {25–34},
numpages = {10},
keywords = {spatial trajectories, spatio-temporal data mining, travel time estimation, urban computing, tensor decomposition},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623681,
author = {Lichman, Moshe and Smyth, Padhraic},
title = {Modeling Human Location Data with Mixtures of Kernel Densities},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623681},
doi = {10.1145/2623330.2623681},
abstract = {Location-based data is increasingly prevalent with the rapid increase and adoption of mobile devices. In this paper we address the problem of learning spatial density models, focusing specifically on individual-level data. Modeling and predicting a spatial distribution for an individual is a challenging problem given both (a) the typical sparsity of data at the individual level and (b) the heterogeneity of spatial mobility patterns across individuals. We investigate the application of kernel density estimation (KDE) to this problem using a mixture model approach that can interpolate between an individual's data and broader patterns in the population as a whole. The mixture-KDE approach is evaluated on two large geolocation/check-in data sets, from Twitter and Gowalla, with comparisons to non-KDE baselines, using both log-likelihood and detection of simulated identity theft as evaluation metrics. Our experimental results indicate that the mixture-KDE method provides a useful and accurate methodology for capturing and predicting individual-level spatial patterns in the presence of noisy and sparse data.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {35–44},
numpages = {10},
keywords = {probabilistic methods, kernel density estimation, anomaly/novelty detection, user modeling, spatial, social media},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623668,
author = {Qu, Meng and Zhu, Hengshu and Liu, Junming and Liu, Guannan and Xiong, Hui},
title = {A Cost-Effective Recommender System for Taxi Drivers},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623668},
doi = {10.1145/2623330.2623668},
abstract = {The GPS technology and new forms of urban geography have changed the paradigm for mobile services. As such, the abundant availability of GPS traces has enabled new ways of doing taxi business. Indeed, recent efforts have been made on developing mobile recommender systems for taxi drivers using Taxi GPS traces. These systems can recommend a sequence of pick-up points for the purpose of maximizing the probability of identifying a customer with the shortest driving distance. However, in the real world, the income of taxi drivers is strongly correlated with the effective driving hours. In other words, it is more critical for taxi drivers to know the actual driving routes to minimize the driving time before finding a customer. To this end, in this paper, we propose to develop a cost-effective recommender system for taxi drivers. The design goal is to maximize their profits when following the recommended routes for finding passengers. Specifically, we first design a net profit objective function for evaluating the potential profits of the driving routes. Then, we develop a graph representation of road networks by mining the historical taxi GPS traces and provide a Brute-Force strategy to generate optimal driving route for recommendation. However, a critical challenge along this line is the high computational cost of the graph based approach. Therefore, we develop a novel recursion strategy based on the special form of the net profit function for searching optimal candidate routes efficiently. Particularly, instead of recommending a sequence of pick-up points and letting the driver decide how to get to those points, our recommender system is capable of providing an entire driving route, and the drivers are able to find a customer for the largest potential profit by following the recommendations. This makes our recommender system more practical and profitable than other existing recommender systems. Finally, we carry out extensive experiments on a real-world data set collected from the San Francisco Bay area and the experimental results clearly validate the effectiveness of the proposed recommender system.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {45–54},
numpages = {10},
keywords = {cost-effective, mobile recommender systems, taxi drivers},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623659,
author = {Park, Yubin and Ghosh, Joydeep},
title = {LUDIA: An Aggregate-Constrained Low-Rank Reconstruction Algorithm to Leverage Publicly Released Health Data},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623659},
doi = {10.1145/2623330.2623659},
abstract = {In the past few years, the government and other agencies have publicly released a prodigious amount of data that can be potentially mined to benefit the society at large. However, data such as health records are typically only provided at aggregated levels (e.g. per State, per Hospital Referral Region, etc.) to protect privacy. Unfortunately aggregation can severely diminish the utility of such data when modeling or analysis is desired at a per-individual basis. So, not surprisingly, despite the increasing abundance of aggregate data, there have been very few successful attempts in exploiting them for individual-level analyses. This paper introduces LUDIA, a novel low-rank approximation algorithm that utilizes aggregation constraints in addition to auxiliary information in order to estimate or "reconstruct" the original individual-level values from aggregate data. If the reconstructed data are statistically similar to the original individual-level data, off-the-shelf individual-level models can be readily and reliably applied for subsequent predictive or descriptive analytics. LUDIA is more robust to nonlinear estimates and random effects than other reconstruction algorithms. It solves a Sylvester equation and leverages multi-level (also known as hierarchical or mixed-effect) modeling approaches efficiently. A novel graphical model is also introduced to provide a probabilistic viewpoint of LUDIA. Experimental results using a Texas inpatient dataset show that individual-level data can be reasonably reconstructed from county-, hospital-, and zip code-level aggregate data. Several factors affecting the reconstruction quality are discussed, along with the implications of this work for current aggregation guidelines.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {55–64},
numpages = {10},
keywords = {multi-level model, data aggregation, low rank approximation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623714,
author = {Mukherjee, Subhabrata and Weikum, Gerhard and Danescu-Niculescu-Mizil, Cristian},
title = {People on Drugs: Credibility of User Statements in Health Communities},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623714},
doi = {10.1145/2623330.2623714},
abstract = {Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity.We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {65–74},
numpages = {10},
keywords = {veracity, objectivity, credibility, trustworthiness, probabilistic graphical models},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623742,
author = {Ghassemi, Marzyeh and Naumann, Tristan and Doshi-Velez, Finale and Brimmer, Nicole and Joshi, Rohit and Rumshisky, Anna and Szolovits, Peter},
title = {Unfolding Physiological State: Mortality Modelling in Intensive Care Units},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623742},
doi = {10.1145/2623330.2623742},
abstract = {Accurate knowledge of a patient's disease state and trajectory is critical in a clinical setting. Modern electronic healthcare records contain an increasingly large amount of data, and the ability to automatically identify the factors that influence patient outcomes stand to greatly improve the efficiency and quality of care.We examined the use of latent variable models (viz. Latent Dirichlet Allocation) to decompose free-text hospital notes into meaningful features, and the predictive power of these features for patient mortality. We considered three prediction regimes: (1) baseline prediction, (2) dynamic (time-varying) outcome prediction, and (3) retrospective outcome prediction. In each, our prediction task differs from the familiar time-varying situation whereby data accumulates; since fewer patients have long ICU stays, as we move forward in time fewer patients are available and the prediction task becomes increasingly difficult.We found that latent topic-derived features were effective in determining patient mortality under three timelines: in-hospital, 30 day post-discharge, and 1 year post-discharge mortality. Our results demonstrated that the latent topic features important in predicting hospital mortality are very different from those that are important in post-discharge mortality. In general, latent topic features were more predictive than structured features, and a combination of the two performed best.The time-varying models that combined latent topic features and baseline features had AUCs that reached 0.85, 0.80, and 0.77 for in-hospital, 30 day post-discharge and 1 year post-discharge mortality respectively. Our results agreed with other work suggesting that the first 24 hours of patient information are often the most predictive of hospital mortality. Retrospective models that used a combination of latent topic features and structured features achieved AUCs of 0.96, 0.82, and 0.81 for in-hospital, 30 day, and 1-year mortality prediction.Our work focuses on the dynamic (time-varying) setting because models from this regime could facilitate an on-going severity stratification system that helps direct care-staff resources and inform treatment strategies.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {75–84},
numpages = {10},
keywords = {graphical and latent variable models, data mining for social good, topic, healthcare and medicine, support vector machines, text},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623754,
author = {Wang, Xiang and Sontag, David and Wang, Fei},
title = {Unsupervised Learning of Disease Progression Models},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623754},
doi = {10.1145/2623330.2623754},
abstract = {Chronic diseases, such as Alzheimer's Disease, Diabetes, and Chronic Obstructive Pulmonary Disease, usually progress slowly over a long period of time, causing increasing burden to the patients, their families, and the healthcare system. A better understanding of their progression is instrumental in early diagnosis and personalized care. Modeling disease progression based on real-world evidence is a very challenging task due to the incompleteness and irregularity of the observations, as well as the heterogeneity of the patient conditions. In this paper, we propose a probabilistic disease progression model that address these challenges. As compared to existing disease progression models, the advantage of our model is three-fold: 1) it learns a continuous-time progression model from discrete-time observations with non-equal intervals; 2) it learns the full progression trajectory from a set of incomplete records that only cover short segments of the progression; 3) it learns a compact set of medical concepts as the bridge between the hidden progression process and the observed medical evidence, which are usually extremely sparse and noisy. We demonstrate the capabilities of our model by applying it to a real-world COPD patient cohort and deriving some interesting clinical insights.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {85–94},
numpages = {10},
keywords = {bayesian network, disease progression modeling, markov jump process, medical informatics},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623639,
author = {Papalexakis, Evangelos E. and Fyshe, Alona and Sidiropoulos, Nicholas D. and Talukdar, Partha Pratim and Mitchell, Tom M. and Faloutsos, Christos},
title = {Good-Enough Brain Model: Challenges, Algorithms and Discoveries in Multi-Subject Experiments},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623639},
doi = {10.1145/2623330.2623639},
abstract = {Given a simple noun such as {em apple}, and a question such as "is it edible?", what processes take place in the human brain? More specifically, given the stimulus, what are the interactions between (groups of) neurons (also known as functional connectivity) and how can we automatically infer those interactions, given measurements of the brain activity? Furthermore, how does this connectivity differ across different human subjects?In this work we present a simple, novel good-enough brain model, or GeBM in short, and a novel algorithm Sparse-SysId, which are able to effectively model the dynamics of the neuron interactions and infer the functional connectivity. Moreover, GeBM is able to simulate basic psychological phenomena such as habituation and priming (whose definition we provide in the main text).We evaluate GeBM by using both synthetic and real brain data. Using the real data, GeBM produces brain activity patterns that are strikingly similar to the real ones, and the inferred functional connectivity is able to provide neuroscientific insights towards a better understanding of the way that neurons interact with each other, as well as detect regularities and outliers in multi-subject brain activity measurements.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {95–104},
numpages = {10},
keywords = {neuroscience, system identification, control theory, brain activity analysis, brain functional connectivity},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623624,
author = {Matsubara, Yasuko and Sakurai, Yasushi and van Panhuis, Willem G. and Faloutsos, Christos},
title = {FUNNEL: Automatic Mining of Spatially Coevolving Epidemics},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623624},
doi = {10.1145/2623330.2623624},
abstract = {Given a large collection of epidemiological data consisting of the count of d contagious diseases for l locations of duration n, how can we find patterns, rules and outliers? For example, the Project Tycho provides open access to the count infections for U.S. states from 1888 to 2013, for 56 contagious diseases (e.g., measles, influenza), which include missing values, possible recording errors, sudden spikes (or dives) of infections, etc. So how can we find a combined model, for all these diseases, locations, and time-ticks? In this paper, we present FUNNEL, a unifying analytical model for large scale epidemiological data, as well as a novel fitting algorithm, FUNNELFIT, which solves the above problem. Our method has the following properties: (a) Sense-making: it detects important patterns of epidemics, such as periodicities, the appearance of vaccines, external shock events, and more; (b) Parameter-free: our modeling framework frees the user from providing parameter values; (c) Scalable: FUNNELFIT is carefully designed to be linear on the input size; (d) General: our model is general and practical, which can be applied to various types of epidemics, including computer-virus propagation, as well as human diseases. Extensive experiments on real data demonstrate that FUNNELFIT does indeed discover important properties of epidemics: (P1) disease seasonality, e.g., influenza spikes in January, Lyme disease spikes in July and the absence of yearly periodicity for gonorrhea; (P2) disease reduction effect, e.g., the appearance of vaccines; (P3) local/state-level sensitivity, e.g., many measles cases in NY; (P4) external shock events, e.g., historical flu pandemics; (P5) detect incongruous values, i.e., data reporting errors.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {105–114},
numpages = {10},
keywords = {automatic mining, time-series, epidemics},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623658,
author = {Ho, Joyce C. and Ghosh, Joydeep and Sun, Jimeng},
title = {Marble: High-Throughput Phenotyping from Electronic Health Records via Sparse Nonnegative Tensor Factorization},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623658},
doi = {10.1145/2623330.2623658},
abstract = {The rapidly increasing availability of electronic health records (EHRs) from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, prognosis, and patient management. Unfortunately, EHR data do not always directly and reliably map to phenotypes, or medical concepts, that clinical researchers need or use. Existing phenotyping approaches typically require labor intensive supervision from medical experts. We propose Marble, a novel sparse non-negative tensor factorization method to derive phenotype candidates with virtually no human supervision. Marble decomposes the observed tensor into two terms, a bias tensor and an interaction tensor. The bias tensor represents the baseline characteristics common amongst the overall population and the interaction tensor defines the phenotypes. We demonstrate the capability of our proposed model on both simulated and patient data from a publicly available clinical database. Our results show that Marble derived phenotypes provide at least a 42.8% reduction in the number of non-zero element and also retains predictive power for classification purposes. Furthermore, the resulting phenotypes and baseline characteristics from real EHR data are consistent with known characteristics of the patient population. Thus it can potentially be used to rapidly characterize, predict, and manage a large number of diseases, thereby promising a novel, data-driven solution that can benefit very large segments of the population.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {115–124},
numpages = {10},
keywords = {application, dimensionality reduction, EHR phenotyping, tensor},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623702,
author = {Chia, Chih-Chun and Syed, Zeeshan},
title = {Scalable Noise Mining in Long-Term Electrocardiographic Time-Series to Predict Death Following Heart Attacks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623702},
doi = {10.1145/2623330.2623702},
abstract = {Cardiac disease is the leading cause of death around the world; with ischemic heart disease alone claiming 7 million lives in 2011. This burden can be attributed, in part, to the absence of biomarkers that can reliably identify high risk patients and match them to treatments that are appropriate for them. In recent clinical studies, we have demonstrated the ability of computation to extract information with substantial prognostic utility that is typically disregarded in time-series data collected from cardiac patients. Of particular interest are subtle variations in long-term electrocardiographic (ECG) data that are usually overlooked as noise but provide a useful assessment of myocardial instability. In multiple clinical cohorts, we have developed the pathophysiological basis for studying probabilistic variations in long-term ECG and demonstrated the ability of this information to effectively risk stratify patients at risk of dying following heart attacks. In this paper, we extend this work and focus on the question of how to reduce its computational complexity for scalable use in large datasets or energy constrained embedded devices. Our basic approach to uncovering pathological structure within the ECG focuses on characterizing beat-to-beat time-warped shape deformations of the ECG using a modified dynamic time-warping (DTW) and Lomb-Scargle periodogram-based algorithm. As part of our efforts to scale this work up, we explore a novel approach to address the quadratic runtime of DTW. We achieve this by developing the idea of adaptive downsampling to reduce the size of the inputs presented to DTW, and describe changes to the dynamic programming problem underlying DTW to exploit adaptively downsampled ECG signals. When evaluated on data from 765 patients in the DISPERSE2-TIMI33 trial, our results show that high morphologic variability is associated with an 8- to 9-fold increased risk of death within 90 days of a heart attack. Moreover, the use of adaptive downsampling with a modified DTW formulation achieves a 7- to almost 20-fold reduction in runtime relative to DTW, without a significant change in biomarker discrimination.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {125–134},
numpages = {10},
keywords = {adaptive downsampling, noise mining, cardiovascular disease, time-warping, electrocardiogram},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623711,
author = {Zhou, Jiayu and Wang, Fei and Hu, Jianying and Ye, Jieping},
title = {From Micro to Macro: Data Driven Phenotyping by Densification of Longitudinal Electronic Medical Records},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623711},
doi = {10.1145/2623330.2623711},
abstract = {Inferring phenotypic patterns from population-scale clinical data is a core computational task in the development of personalized medicine. One important source of data on which to conduct this type of research is patient Electronic Medical Records (EMR). However, the patient EMRs are typically sparse and noisy, which creates significant challenges if we use them directly to represent patient phenotypes. In this paper, we propose a data driven phenotyping framework called Pacifier (PAtient reCord densIFIER), where we interpret the longitudinal EMR data of each patient as a sparse matrix with a feature dimension and a time dimension, and derive more robust patient phenotypes by exploring the latent structure of those matrices. Specifically, we assume that each derived phenotype is composed of a subset of the medical features contained in original patient EMR, whose value evolves smoothly over time. We propose two formulations to achieve such goal. One is Individual Basis Approach (IBA), which assumes the phenotypes are different for every patient. The other is Shared Basis Approach (SBA), which assumes the patient population shares a common set of phenotypes. We develop an efficient optimization algorithm that is capable of resolving both problems efficiently. Finally we validate Pacifier on two real world EMR cohorts for the tasks of early prediction of Congestive Heart Failure (CHF) and End Stage Renal Disease (ESRD). Our results show that the predictive performance in both tasks can be improved significantly by the proposed algorithms (average AUC score improved from 0.689 to 0.816 on CHF, and from 0.756 to 0.838 on ESRD respectively, on diagnosis group granularity). We also illustrate some interesting phenotypes derived from our data.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {135–144},
numpages = {10},
keywords = {phenotyping, sparse learning, densification, matrix completion, medical informatics},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623755,
author = {Wang, Fei and Zhang, Ping and Qian, Buyue and Wang, Xiang and Davidson, Ian},
title = {Clinical Risk Prediction with Multilinear Sparse Logistic Regression},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623755},
doi = {10.1145/2623330.2623755},
abstract = {Logistic regression is one core predictive modeling technique that has been used extensively in health and biomedical problems. Recently a lot of research has been focusing on enforcing sparsity on the learned model to enhance its effectiveness and interpretability, which results in sparse logistic regression model. However, no matter the original or sparse logistic regression, they require the inputs to be in vector form. This limits the applicability of logistic regression in the problems when the data cannot be naturally represented vectors (e.g., functional magnetic resonance imaging and electroencephalography signals). To handle the cases when the data are in the form of multi-dimensional arrays, we propose MulSLR: Multilinear Sparse Logistic Regression. MulSLR can be viewed as a high order extension of sparse logistic regression. Instead of solving one classification vector as in conventional logistic regression, we solve for K classification vectors in MulSLR (K is the number of modes in the data). We propose a block proximal descent approach to solve the problem and prove its convergence. The convergence rate of the proposed algorithm is also analyzed. Finally we validate the efficiency and effectiveness of MulSLR on predicting the onset risk of patients with Alzheimer's disease and heart failure.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {145–154},
numpages = {10},
keywords = {multilinear, proximal gradient, healthcare, logistic regression},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623750,
author = {Ross, James C. and Castaldi, Peter J. and Cho, Michael H. and Dy, Jennifer G.},
title = {Dual Beta Process Priors for Latent Cluster Discovery in Chronic Obstructive Pulmonary Disease},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623750},
doi = {10.1145/2623330.2623750},
abstract = {Chronic obstructive pulmonary disease (COPD) is a lung disease characterized by airflow limitation usually associated with an inflammatory response to noxious particles, such as cigarette smoke. COPD is currently the third leading cause of death in the United States and is the only leading cause of death that is increasing in prevalence. It also represents an enormous financial burden to society, costing tens of billions of dollars annually in the U.S. It is widely accepted by the medical community that COPD is a heterogeneous disease, with substantial evidence indicating that genetic variation contributes to varying levels of disease susceptibility. This heterogeneity makes it difficult to predict health decline and develop targeted treatments for better patient care. Although researchers have made several attempts to discover disease subtypes, results have been inconclusive, in part because standard clustering methods have not properly dealt with disease manifestations that may worsen with increased exposure. In this paper we introduce a transformative way of looking at the COPD subtyping task. Specifically, we model the relationship between risk factors (such as age and smoke exposure) and manifestations of disease severity using Gaussian Processes, which allow us to represent so-called "disease trajectories". We also posit that individuals can be associated with multiple disease types (latent clusters), which we assume are influenced by genetics. Furthermore, we predict that only subsets of the numerous disease-related quantitative features are useful for describing each latent subtype. We model these associations using two separate beta process priors, and we describe a variational inference approach to discover the most probable latent cluster assignments. Results are validated with associations to genetic markers.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {155–162},
numpages = {8},
keywords = {beta processes, gaussian processes, disease trajectories, Bayesian nonparametrics, latent clusters},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623616,
author = {Yuan, Quan and Cong, Gao and Lin, Chin-Yew},
title = {COM: A Generative Model for Group Recommendation},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623616},
doi = {10.1145/2623330.2623616},
abstract = {With the rapid development of online social networks, a growing number of people are willing to share their group activities, e.g. having dinners with colleagues, and watching movies with spouses. This motivates the studies on group recommendation, which aims to recommend items for a group of users. Group recommendation is a challenging problem because different group members have different preferences, and how to make a trade-off among their preferences for recommendation is still an open problem. In this paper, we propose a probabilistic model named COM (COnsensus Model) to model the generative process of group activities, and make group recommendations. Intuitively, users in a group may have different influences, and those who are expert in topics relevant to the group are usually more influential. In addition, users in a group may behave differently as group members from as individuals. COM is designed based on these intuitions, and is able to incorporate both users' selection history and personal considerations of content factors. When making recommendations, COM estimates the preference of a group to an item by aggregating the preferences of the group members with different weights. We conduct extensive experiments on four datasets, and the results show that the proposed model is effective in making group recommendations, and outperforms baseline methods significantly.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {163–172},
numpages = {10},
keywords = {collaborative filtering, group recommendation, topic models},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623663,
author = {Charlin, Laurent and Zemel, Richard S. and Larochelle, Hugo},
title = {Leveraging User Libraries to Bootstrap Collaborative Filtering},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623663},
doi = {10.1145/2623330.2623663},
abstract = {We introduce a novel graphical model, the collaborative score topic model (CSTM), for personal recommendations of textual documents. CSTM's chief novelty lies in its learned model of individual libraries, or sets of documents, associated with each user. Overall, CSTM is a joint directed probabilistic model of user-item scores (ratings), and the textual side information in the user libraries and the items. Creating a generative description of scores and the text allows CSTM to perform well in a wide variety of data regimes, smoothly combining the side information with observed ratings as the number of ratings available for a given user ranges from none to many. Experiments on real-world datasets demonstrate CSTM's performance. We further demonstrate its utility in an application for personal recommendations of posters which we deployed at the NIPS 2013 conference.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {173–182},
numpages = {10},
keywords = {cold start, side information, collaborative filtering, topic modeling, document recommendations},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623700,
author = {Gu, Yupeng and Sun, Yizhou and Jiang, Ning and Wang, Bingyu and Chen, Ting},
title = {Topic-Factorized Ideal Point Estimation Model for Legislative Voting Network},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623700},
doi = {10.1145/2623330.2623700},
abstract = {Ideal point estimation that estimates legislators' ideological positions and understands their voting behavior has attracted studies from political science and computer science. Typically, a legislator is assigned a global ideal point based on her voting or other social behavior. However, it is quite normal that people may have different positions on different policy dimensions. For example, some people may be more liberal on economic issues while more conservative on cultural issues. In this paper, we propose a novel topic-factorized ideal point estimation model for a legislative voting network in a unified framework. First, we model the ideal points of legislators and bills for each topic instead of assigning them to a global one. Second, the generation of topics are guided by the voting matrix in addition to the text information contained in bills. A unified model that combines voting behavior modeling and topic modeling is presented, and an iterative learning algorithm is proposed to learn the topics of bills as well as the topic-factorized ideal points of legislators and bills. By comparing with the state-of-the-art ideal point estimation models, our method has a much better explanation power in terms of held-out log-likelihood and other measures. Besides, case studies show that the topic-factorized ideal points coincide with human intuition. Finally, we illustrate how to use these topic-factorized ideal points to predict voting results for unseen bills.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {183–192},
numpages = {10},
keywords = {voting prediction, topic model, legislative voting network, ideal point estimation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623758,
author = {Diao, Qiming and Qiu, Minghui and Wu, Chao-Yuan and Smola, Alexander J. and Jiang, Jing and Wang, Chong},
title = {Jointly Modeling Aspects, Ratings and Sentiments for Movie Recommendation (JMARS)},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623758},
doi = {10.1145/2623330.2623758},
abstract = {Recommendation and review sites offer a wealth of information beyond ratings. For instance, on IMDb users leave reviews, commenting on different aspects of a movie (e.g. actors, plot, visual effects), and expressing their sentiments (positive or negative) on these aspects in their reviews. This suggests that uncovering aspects and sentiments will allow us to gain a better understanding of users, movies, and the process involved in generating ratings.The ability to answer questions such as "Does this user care more about the plot or about the special effects?" or "What is the quality of the movie in terms of acting?" helps us to understand why certain ratings are generated. This can be used to provide more meaningful recommendations.In this work we propose a probabilistic model based on collaborative filtering and topic modeling. It allows us to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on a per-aspect basis. Unlike prior work our approach is entirely unsupervised and does not require knowledge of the aspect specific ratings or genres for inference.We evaluate our model on a live copy crawled from IMDb. Our model offers superior performance by joint modeling. Moreover, we are able to address the cold start problem -- by utilizing the information inherent in reviews our model demonstrates improvement for new users and movies.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {193–202},
numpages = {10},
keywords = {collaborative filtering, sentiment analysis, topic models, integrated modeling},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623610,
author = {Hasan, Mahbub and Kashyap, Abhijith and Hristidis, Vagelis and Tsotras, Vassilis},
title = {User Effort Minimization through Adaptive Diversification},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623610},
doi = {10.1145/2623330.2623610},
abstract = {Ambiguous queries, which are typical on search engines and recommendation systems, often return a large number of results from multiple interpretations. Given that many users often perform their searches on limited size screens (e.g. mobile phones), an important problem is which results to display first. Recent work has suggested displaying a set of results (Top-k) based on their relevance score with respect to the query and their diversity with respect to each other. However, previous works balance relevance and diversity mostly by a predefined fixed way. In this paper, we show that for different search tasks there is a different ideal balance of relevance and diversity. We propose a principled method for adaptive diversification of query results that minimizes the user effort to find the desired results, by dynamically balancing the relevance and diversity at each query step (e.g. when refining the query or viewing the next page of results). We introduce a navigation cost model as a means to estimate the effort required to navigate the query-results, and show that the problem of estimating the ideal amount of diversification at each step is NP-Hard. We propose an efficient approximate algorithm to select a near-optimal subset of the query results that minimizes the expected user effort. Finally we demonstrate the efficacy and efficiency of our solution in minimizing user effort, compared to state-of-the-art ranking methods, by means of an extensive experimental evaluation and a comprehensive user study on Amazon Mechanical Turk.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {203–212},
numpages = {10},
keywords = {user effort minimization, mobile applications, diversity},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623652,
author = {He, Xiao and Feng, Jing and Konte, Bettina and Mai, Son T. and Plant, Claudia},
title = {Relevant Overlapping Subspace Clusters on Categorical Data},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623652},
doi = {10.1145/2623330.2623652},
abstract = {Clustering categorical data poses some unique challenges: Due to missing order and spacing among the categories, selecting a suitable similarity measure is a difficult task. Many existing techniques require the user to specify input parameters which are difficult to estimate. Moreover, many techniques are limited to detect clusters in the full-dimensional data space. Only few methods exist for subspace clustering and they produce highly redundant results. Therefore, we propose ROCAT (Relevant Overlapping Subspace Clusters on Categorical Data), a novel technique based on the idea of data compression. Following the Minimum Description Length principle, ROCAT automatically detects the most relevant subspace clusters without any input parameter. The relevance of each cluster is validated by its contribution to compress the data. Optimizing the trade-off between goodness-of-fit and model complexity, ROCAT automatically determines a meaningful number of clusters to represent the data. ROCAT is especially designed to detect subspace clusters on categorical data which may overlap in objects and/or attributes; i.e. objects can be assigned to different clusters in different subspaces and attributes may contribute to different subspaces containing clusters. ROCAT naturally avoids undesired redundancy in clusters and subspaces by allowing overlap only if it improves the compression rate. Extensive experiments demonstrate the effectiveness and efficiency of our approach.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {213–222},
numpages = {10},
keywords = {relevant subspace clustering, categorical data, minimum description length},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623695,
author = {Dundar, Murat and Yerebakan, Halid Ziya and Rajwa, Bartek},
title = {Batch Discovery of Recurring Rare Classes toward Identifying Anomalous Samples},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623695},
doi = {10.1145/2623330.2623695},
abstract = {We present a clustering algorithm for discovering rare yet significant recurring classes across a batch of samples in the presence of random effects. We model each sample data by an infinite mixture of Dirichlet-process Gaussian-mixture models (DPMs) with each DPM representing the noisy realization of its corresponding class distribution in a given sample. We introduce dependencies across multiple samples by placing a global Dirichlet process prior over individual DPMs. This hierarchical prior introduces a sharing mechanism across samples and allows for identifying local realizations of classes across samples. We use collapsed Gibbs sampler for inference to recover local DPMs and identify their class associations. We demonstrate the utility of the proposed algorithm, processing a flow cytometry data set containing two extremely rare cell populations, and report results that significantly outperform competing techniques. The source code of the proposed algorithm is available on the web via the link:http://cs.iupui.edu/~dundar/aspire.htm.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {223–232},
numpages = {10},
keywords = {random effects, hierarchical dirichlet process, recurring classes, rare classes, batch clustering, anomaly detection},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623715,
author = {Yin, Jianhua and Wang, Jianyong},
title = {A Dirichlet Multinomial Mixture Model-Based Approach for Short Text Clustering},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623715},
doi = {10.1145/2623330.2623715},
abstract = {Short text clustering has become an increasingly important task with the popularity of social media like Twitter, Google+, and Facebook. It is a challenging problem due to its sparse, high-dimensional, and large-volume characteristics. In this paper, we proposed a collapsed Gibbs Sampling algorithm for the Dirichlet Multinomial Mixture model for short text clustering (abbr. to GSDMM). We found that GSDMM can infer the number of clusters automatically with a good balance between the completeness and homogeneity of the clustering results, and is fast to converge. GSDMM can also cope with the sparse and high-dimensional problem of short texts, and can obtain the representative words of each cluster. Our extensive experimental study shows that GSDMM can achieve significantly better performance than three other clustering models.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {233–242},
numpages = {10},
keywords = {gibbs sampling, short text clustering, dirichlet multinomial mixture},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623725,
author = {Z\"{u}fle, Andreas and Emrich, Tobias and Schmid, Klaus Arthur and Mamoulis, Nikos and Zimek, Arthur and Renz, Matthias},
title = {Representative Clustering of Uncertain Data},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623725},
doi = {10.1145/2623330.2623725},
abstract = {This paper targets the problem of computing meaningful clusterings from uncertain data sets. Existing methods for clustering uncertain data compute a single clustering without any indication of its quality and reliability; thus, decisions based on their results are questionable. In this paper, we describe a framework, based on possible-worlds semantics; when applied on an uncertain dataset, it computes a set of representative clusterings, each of which has a probabilistic guarantee not to exceed some maximum distance to the ground truth clustering, i.e., the clustering of the actual (but unknown) data. Our framework can be combined with any existing clustering algorithm and it is the first to provide quality guarantees about its result. In addition, our experimental evaluation shows that our representative clusterings have a much smaller deviation from the ground truth clustering than existing approaches, thus reducing the effect of uncertainty.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {243–252},
numpages = {10},
keywords = {possible worlds, clustering, uncertain data},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623734,
author = {G\"{u}nnemann, Stephan and F\"{a}rber, Ines and R\"{u}diger, Matthias and Seidl, Thomas},
title = {SMVC: Semi-Supervised Multi-View Clustering in Subspace Projections},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623734},
doi = {10.1145/2623330.2623734},
abstract = {Since data is often multi-faceted in its very nature, it might not adequately be summarized by just a single clustering. To better capture the data's complexity, methods aiming at the detection of multiple, alternative clusterings have been proposed. Independent of this research area, semi-supervised clustering techniques have shown to substantially improve clustering results for single-view clustering by integrating prior knowledge. In this paper, we join both research areas and present a solution for integrating prior knowledge in the process of detecting multiple clusterings.We propose a Bayesian framework modeling multiple clusterings of the data by multiple mixture distributions, each responsible for an individual set of relevant dimensions. In addition, our model is able to handle prior knowledge in the form of instance-level constraints indicating which objects should or should not be grouped together. Since a priori the assignment of constraints to specific views is not necessarily known, our technique automatically determines their membership. For efficient learning, we propose the algorithm SMVC using variational Bayesian methods. With experiments on various real-world data, we demonstrate SMVC's potential to detect multiple clustering views and its capability to improve the result by exploiting prior knowledge.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {253–262},
numpages = {10},
keywords = {constraints, semi-supervised learning, subspace clustering},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623651,
author = {Prabhu, Yashoteja and Varma, Manik},
title = {FastXML: A Fast, Accurate and Stable Tree-Classifier for Extreme Multi-Label Learning},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623651},
doi = {10.1145/2623330.2623651},
abstract = {The objective in extreme multi-label classification is to learn a classifier that can automatically tag a data point with the most relevant subset of labels from a large label set. Extreme multi-label classification is an important research problem since not only does it enable the tackling of applications with many labels but it also allows the reformulation of ranking problems with certain advantages over existing formulations. Our objective, in this paper, is to develop an extreme multi-label classifier that is faster to train and more accurate at prediction than the state-of-the-art Multi-label Random Forest (MLRF) algorithm [2] and the Label Partitioning for Sub-linear Ranking (LPSR) algorithm [35]. MLRF and LPSR learn a hierarchy to deal with the large number of labels but optimize task independent measures, such as the Gini index or clustering error, in order to learn the hierarchy. Our proposed FastXML algorithm achieves significantly higher accuracies by directly optimizing an nDCG based ranking loss function. We also develop an alternating minimization algorithm for efficiently optimizing the proposed formulation. Experiments reveal that FastXML can be trained on problems with more than a million labels on a standard desktop in eight hours using a single core and in an hour using multiple cores.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {263–272},
numpages = {10},
keywords = {extreme classification, ranking, multi-label learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623689,
author = {Zhai, Shaodan and Xia, Tian and Wang, Shaojun},
title = {A Multi-Class Boosting Method with Direct Optimization},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623689},
doi = {10.1145/2623330.2623689},
abstract = {We present a direct multi-class boosting (DMCBoost) method for classification with the following properties: (i) instead of reducing the multi-class classification task to a set of binary classification tasks, DMCBoost directly solves the multi-class classification problem, and only requires very weak base classifiers; (ii) DMCBoost builds an ensemble classifier by directly optimizing the non-convex performance measures, including the empirical classification error and margin functions, without resorting to any upper bounds or approximations. As a non-convex optimization method, DMCBoost shows competitive or better results than state-of-the-art convex relaxation boosting methods, and it performs especially well on the noisy cases.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {273–282},
numpages = {10},
keywords = {supervised learning, noise tolerance, boosting, direct optimization, classification},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623665,
author = {Liu, Yashu and Wang, Jie and Ye, Jieping},
title = {An Efficient Algorithm for Weak Hierarchical Lasso},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623665},
doi = {10.1145/2623330.2623665},
abstract = {Linear regression is a widely used tool in data mining and machine learning. In many applications, fitting a regression model with only linear effects may not be sufficient for predictive or explanatory purposes. One strategy which has recently received increasing attention in statistics is to include feature interactions to capture the nonlinearity in the regression model. Such model has been applied successfully in many biomedical applications. One major challenge in the use of such model is that the data dimensionality is significantly higher than the original data, resulting in the small sample size large dimension problem. Recently, weak hierarchical Lasso, a sparse interaction regression model, is proposed that produces sparse and hierarchical structured estimator by exploiting the Lasso penalty and a set of hierarchical constraints. However, the hierarchical constraints make it a non-convex problem and the existing method finds the solution of its convex relaxation, which needs additional conditions to guarantee the hierarchical structure. In this paper, we propose to directly solve the non-convex weak hierarchical Lasso by making use of the GIST (General Iterative Shrinkage and Thresholding) optimization framework which has been shown to be efficient for solving non-convex sparse formulations. The key step in GIST is to compute a sequence of proximal operators. One of our key technical contributions is to show that the proximal operator associated with the non-convex weak hierarchical Lasso admits a closed form solution. However, a naive approach for solving each subproblem of the proximal operator leads to a quadratic time complexity, which is not desirable for large size problems. To this end, we further develop an efficient algorithm for computing the subproblems with a linearithmic time complexity. We have conducted extensive experiments on both synthetic and real data sets. Results show that our proposed algorithm is much more efficient and effective than its convex relaxation.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {283–292},
numpages = {10},
keywords = {weak hierarchical lasso, sparse learning, proximal operator, non-convex},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623712,
author = {Sahoo, Doyen and Hoi, Steven C.H. and Li, Bin},
title = {Online Multiple Kernel Regression},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623712},
doi = {10.1145/2623330.2623712},
abstract = {Kernel-based regression represents an important family of learning techniques for solving challenging regression tasks with non-linear patterns. Despite being studied extensively, most of the existing work suffers from two major drawbacks: (i) they are often designed for solving regression tasks in a batch learning setting, making them not only computationally inefficient and but also poorly scalable in real-world applications where data arrives sequentially; and (ii) they usually assume a fixed kernel function is given prior to the learning task, which could result in poor performance if the chosen kernel is inappropriate. To overcome these drawbacks, this paper presents a novel scheme of Online Multiple Kernel Regression (OMKR), which sequentially learns the kernel-based regressor in an online and scalable fashion, and dynamically explore a pool of multiple diverse kernels to avoid suffering from a single fixed poor kernel so as to remedy the drawback of manual/heuristic kernel selection. The OMKR problem is more challenging than regular kernel-based regression tasks since we have to on-the-fly determine both the optimal kernel-based regressor for each individual kernel and the best combination of the multiple kernel regressors. In this paper, we propose a family of OMKR algorithms for regression and discuss their application to time series prediction tasks. We also analyze the theoretical bounds of the proposed OMKR method and conduct extensive experiments to evaluate its empirical performance on both real-world regression and times series prediction tasks.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {293–302},
numpages = {10},
keywords = {multiple kernel learning, online learning, kernel regression, time series prediction},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623676,
author = {Xie, Sihong and Gao, Jing and Fan, Wei and Turaga, Deepak and Yu, Philip S.},
title = {Class-Distribution Regularized Consensus Maximization for Alleviating Overfitting in Model Combination},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623676},
doi = {10.1145/2623330.2623676},
abstract = {In data mining applications such as crowdsourcing and privacy-preserving data mining, one may wish to obtain consolidated predictions out of multiple models without access to features of the data. Besides, multiple models usually carry complementary predictive information, model combination can potentially provide more robust and accurate predictions by correcting independent errors from individual models. Various methods have been proposed to combine predictions such that the final predictions are maximally agreed upon by multiple base models. Though this maximum consensus principle has been shown to be successful, simply maximizing consensus can lead to less discriminative predictions and overfit the inevitable noise due to imperfect base models. We argue that proper regularization for model combination approaches is needed to alleviate such overfitting effect. Specifically, we analyze the hypothesis spaces of several model combination methods and identify the trade-off between model consensus and generalization ability. We propose a novel model called Regularized Consensus Maximization (RCM), which is formulated as an optimization problem to combine the maximum consensus and large margin principles. We theoretically show that RCM has a smaller upper bound on generalization error compared to the version without regularization. Experiments show that the proposed algorithm outperforms a wide spectrum of state-of-the-art model combination methods on 11 tasks.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {303–312},
numpages = {10},
keywords = {large margin, ensemble, generalization error},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623710,
author = {Zhang, Teng and Zhou, Zhi-Hua},
title = {Large Margin Distribution Machine},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623710},
doi = {10.1145/2623330.2623710},
abstract = {Support vector machine (SVM) has been one of the most popular learning algorithms, with the central idea of maximizing the minimum margin, i.e., the smallest distance from the instances to the classification boundary. Recent theoretical results, however, disclosed that maximizing the minimum margin does not necessarily lead to better generalization performances, and instead, the margin distribution has been proven to be more crucial. In this paper, we propose the Large margin Distribution Machine (LDM), which tries to achieve a better generalization performance by optimizing the margin distribution. We characterize the margin distribution by the first- and second-order statistics, i.e., the margin mean and variance. The LDM is a general learning approach which can be used in any place where SVM can be applied, and its superiority is verified both theoretically and empirically in this paper.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {313–322},
numpages = {10},
keywords = {minimum margin, margin distribution, classification},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623678,
author = {Qian, Qi and Hu, Juhua and Jin, Rong and Pei, Jian and Zhu, Shenghuo},
title = {Distance Metric Learning Using Dropout: A Structured Regularization Approach},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623678},
doi = {10.1145/2623330.2623678},
abstract = {Distance metric learning (DML) aims to learn a distance metric better than Euclidean distance. It has been successfully applied to various tasks, e.g., classification, clustering and information retrieval. Many DML algorithms suffer from the over-fitting problem because of a large number of parameters to be determined in DML. In this paper, we exploit the dropout technique, which has been successfully applied in deep learning to alleviate the over-fitting problem, for DML. Different from the previous studies that only apply dropout to training data, we apply dropout to both the learned metrics and the training data. We illustrate that application of dropout to DML is essentially equivalent to matrix norm based regularization. Compared with the standard regularization scheme in DML, dropout is advantageous in simulating the structured regularizers which have shown consistently better performance than non structured regularizers. We verify, both empirically and theoretically, that dropout is effective in regulating the learned metric to avoid the over-fitting problem. Last, we examine the idea of wrapping the dropout technique in the state-of-art DML methods and observe that the dropout technique can significantly improve the performance of the original DML methods.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {323–332},
numpages = {10},
keywords = {dropout, distance metric learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623648,
author = {Goh, Siong Thye and Rudin, Cynthia},
title = {Box Drawings for Learning with Imbalanced Data},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623648},
doi = {10.1145/2623330.2623648},
abstract = {The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly imbalanced classification problems. The classifiers are disjunctions of conjunctions, and are created as unions of parallel axis rectangles around the positive examples, and thus have the benefit of being interpretable. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. Regularization is introduced to improve generalization performance. The second method uses an approximation in order to assist with scalability. Specifically, it follows a textit{characterize then discriminate} approach, where the positive class is characterized first by boxes, and then each box boundary becomes a separate discriminative classifier. This method has the computational advantages that it can be easily parallelized, and considers only the relevant regions of feature space.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {333–342},
numpages = {10},
keywords = {classification, decision trees, imbalanced data},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623661,
author = {Tsai, Cheng-Hao and Lin, Chieh-Yen and Lin, Chih-Jen},
title = {Incremental and Decremental Training for Linear Classification},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623661},
doi = {10.1145/2623330.2623661},
abstract = {In classification, if a small number of instances is added or removed, incremental and decremental techniques can be applied to quickly update the model. However, the design of incremental and decremental algorithms involves many considerations. In this paper, we focus on linear classifiers including logistic regression and linear SVM because of their simplicity over kernel or other methods. By applying a warm start strategy, we investigate issues such as using primal or dual formulation, choosing optimization methods, and creating practical implementations. Through theoretical analysis and practical experiments, we conclude that a warm start setting on a high-order optimization method for primal formulations is more suitable than others for incremental and decremental learning of linear classification.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {343–352},
numpages = {10},
keywords = {decremental learning, warm start, linear classification, incremental learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623618,
author = {Zhang, Junbo and Tian, Guangjian and Mu, Yadong and Fan, Wei},
title = {Supervised Deep Learning with Auxiliary Networks},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623618},
doi = {10.1145/2623330.2623618},
abstract = {Deep learning well demonstrates its potential in learning latent feature representations. Recent years have witnessed an increasing enthusiasm for regularizing deep neural networks by incorporating various side information, such as user-provided labels or pairwise constraints. However, the effectiveness and parameter sensitivity of such algorithms have been major obstacles for putting them into practice. The major contribution of our work is the exposition of a novel supervised deep learning algorithm, which distinguishes from two unique traits. First, it regularizes the network construction by utilizing similarity or dissimilarity constraints between data pairs, rather than sample-specific annotations. Such kind of side information is more flexible and greatly mitigates the workload of annotators. Secondly, unlike prior works, our proposed algorithm decouples the supervision information and intrinsic data structure. We design two heterogeneous networks, each of which encodes either supervision or unsupervised data structure respectively. Specifically, we term the supervision-oriented network as "auxiliary network" since it is principally used for facilitating the parameter learning of the other one and will be removed when handling out-of-sample data. The two networks are complementary to each other and bridged by enforcing the correlation of their parameters. We name the proposed algorithm SUpervision-Guided AutoencodeR (SUGAR). Comparing prior works on unsupervised deep networks and supervised learning, SUGAR better balances numerical tractability and the flexible utilization of supervision information. The classification performance on MNIST digits and eight benchmark datasets demonstrates that SUGAR can effectively improve the performance by using the auxiliary networks, on both shallow and deep architectures. Particularly, when multiple SUGARs are stacked, the performance is significantly boosted. On the selected benchmarks, ours achieve up to 11.35% relative accuracy improvement compared to the state-of-the-art models.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {353–361},
numpages = {9},
keywords = {deep neural networks, supervision, autoencoder},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623699,
author = {Babaie, Tahereh and Chawla, Sanjay and Abeysuriya, Romesh},
title = {Sleep Analytics and Online Selective Anomaly Detection},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623699},
doi = {10.1145/2623330.2623699},
abstract = {We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to model a specific scenario emerging from research in sleep science. Scientists have segmented sleep into several stages and stage two is characterized by two patterns (or anomalies) in the EEG time series recorded on sleep subjects. These two patterns are sleep spindle (SS) and K-complex. The OSAD problem was introduced to design a residual system, where all anomalies (known and unknown) are detected but the system only triggers an alarm when non-SS anomalies appear. The solution of the OSAD problem required us to combine techniques from both data mining and control theory. Experiments on data from real subjects attest to the effectiveness of our approach.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {362–371},
numpages = {10},
keywords = {sleep EEG anomalies, anomaly/novelty detection, dynamic residue model, mining rich data types},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623719,
author = {Yu, Rose and He, Xinran and Liu, Yan},
title = {GLAD: Group Anomaly Detection in Social Media Analysis},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623719},
doi = {10.1145/2623330.2623719},
abstract = {Traditional anomaly detection on social media mostly focuses on individual point anomalies while anomalous phenomena usually occur in groups. Therefore it is valuable to study the collective behavior of individuals and detect group anomalies. Existing group anomaly detection approaches rely on the assumption that the groups are known, which can hardly be true in real world social media applications. In this paper, we take a generative approach by proposing a hierarchical Bayes model: Group Latent Anomaly Detection (GLAD) model. GLAD takes both pair-wise and point-wise data as input, automatically infers the groups and detects group anomalies simultaneously. To account for the dynamic properties of the social media data, we further generalize GLAD to its dynamic extension d-GLAD. We conduct extensive experiments to evaluate our models on both synthetic and real world datasets. The empirical results demonstrate that our approach is effective and robust in discovering latent groups and detecting group anomalies.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {372–381},
numpages = {10},
keywords = {hierarchical bayes modeling, anomaly detection, social media analysis},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623709,
author = {Cheng, Dehua and Bahadori, Mohammad Taha and Liu, Yan},
title = {FBLG: A Simple and Effective Approach for Temporal Dependence Discovery from Time Series Data},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623709},
doi = {10.1145/2623330.2623709},
abstract = {Discovering temporal dependence structure from multivariate time series has established its importance in many applications. We observe that when we look in reversed order of time, the temporal dependence structure of the time series is usually preserved after switching the roles of cause and effect. Inspired by this observation, we create a new time series by reversing the time stamps of original time series and combine both time series to improve the performance of temporal dependence recovery. We also provide theoretical justification for the proposed algorithm for several existing time series models. We test our approach on both synthetic and real world datasets. The experimental results confirm that this surprisingly simple approach is indeed effective under various circumstances.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {382–391},
numpages = {10},
keywords = {time series analysis, generalized linear model, granger causality},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623613,
author = {Grabocka, Josif and Schilling, Nicolas and Wistuba, Martin and Schmidt-Thieme, Lars},
title = {Learning Time-Series Shapelets},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623613},
doi = {10.1145/2623330.2623613},
abstract = {Shapelets are discriminative sub-sequences of time series that best predict the target variable. For this reason, shapelet discovery has recently attracted considerable interest within the time-series research community. Currently shapelets are found by evaluating the prediction qualities of numerous candidates extracted from the series segments. In contrast to the state-of-the-art, this paper proposes a novel perspective in terms of learning shapelets. A new mathematical formalization of the task via a classification objective function is proposed and a tailored stochastic gradient learning algorithm is applied. The proposed method enables learning near-to-optimal shapelets directly without the need to try out lots of candidates. Furthermore, our method can learn true top-K shapelets by capturing their interaction. Extensive experimentation demonstrates statistically significant improvement in terms of wins and ranks against 13 baselines over 28 time-series datasets.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {392–401},
numpages = {10},
keywords = {supervised feature extraction, shapelets, time-series classification},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623694,
author = {Ghalwash, Mohamed F. and Radosavljevic, Vladan and Obradovic, Zoran},
title = {Utilizing Temporal Patterns for Estimating Uncertainty in Interpretable Early Decision Making},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623694},
doi = {10.1145/2623330.2623694},
abstract = {Early classification of time series is prevalent in many time-sensitive applications such as, but not limited to, early warning of disease outcome and early warning of crisis in stock market. textcolor{black}{ For example,} early diagnosis allows physicians to design appropriate therapeutic strategies at early stages of diseases. However, practical adaptation of early classification of time series requires an easy to understand explanation (interpretability) and a measure of confidence of the prediction results (uncertainty estimates). These two aspects were not jointly addressed in previous time series early classification studies, such that a difficult choice of selecting one of these aspects is required. In this study, we propose a simple and yet effective method to provide uncertainty estimates for an interpretable early classification method. The question we address here is "how to provide estimates of uncertainty in regard to interpretable early prediction." In our extensive evaluation on twenty time series datasets we showed that the proposed method has several advantages over the state-of-the-art method that provides reliability estimates in early classification. Namely, the proposed method is more effective than the state-of-the-art method, is simple to implement, and provides interpretable results.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {402–411},
numpages = {10},
keywords = {reliability, time series, uncertainty, interpretability, earliness},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623609,
author = {Shao, Junming and Ahmadi, Zahra and Kramer, Stefan},
title = {Prototype-Based Learning on Concept-Drifting Data Streams},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623609},
doi = {10.1145/2623330.2623609},
abstract = {Data stream mining has gained growing attentions due to its wide emerging applications such as target marketing, email filtering and network intrusion detection. In this paper, we propose a prototype-based classification model for evolving data streams, called SyncStream, which dynamically models time-changing concepts and makes predictions in a local fashion. Instead of learning a single model on a sliding window or ensemble learning, SyncStream captures evolving concepts by dynamically maintaining a set of prototypes in a new data structure called the P-tree. The prototypes are obtained by error-driven representativeness learning and synchronization-inspired constrained clustering. To identify abrupt concept drift in data streams, PCA and statistics based heuristic approaches are employed. SyncStream has several attractive benefits: (a) It is capable of dynamically modeling evolving concepts from even a small set of prototypes and is robust against noisy examples. (b) Owing to synchronization-based constrained clustering and the P-Tree, it supports an efficient and effective data representation and maintenance. (c) Gradual and abrupt concept drift can be effectively detected. Empirical results shows that our method achieves good predictive performance compared to state-of-the-art algorithms and that it requires much less time than another instance-based stream mining algorithm.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {412–421},
numpages = {10},
keywords = {synchronization, concept drift, data stream, classification},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623735,
author = {Yu, Yanwei and Cao, Lei and Rundensteiner, Elke A. and Wang, Qin},
title = {Detecting Moving Object Outliers in Massive-Scale Trajectory Streams},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623735},
doi = {10.1145/2623330.2623735},
abstract = {The detection of abnormal moving objects over high-volume trajectory streams is critical for real time applications ranging from military surveillance to transportation management. Yet this problem remains largely unexplored. In this work, we first propose classes of novel trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real time applications. Our theoretical analysis and empirical study on the Beijing Taxi and GMTI (Ground Moving Target Indicator) datasets demonstrate its effectiveness in capturing abnormal moving objects. Furthermore we propose a general strategy for efficiently detecting the new outlier classes. It features three fundamental optimization principles designed to minimize the detection costs. Our comprehensive experimental studies demonstrate that our proposed strategy drives the detection costs 100-fold down into practical realm for applications producing high volume trajectory streams to utilize.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {422–431},
numpages = {10},
keywords = {outlier, trajectory stream, moving object},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623751,
author = {Aggarwal, Charu C.},
title = {The Setwise Stream Classification Problem},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623751},
doi = {10.1145/2623330.2623751},
abstract = {In many applications, classification labels may not be associated with a single instance of records, but may be associated with a data set of records. The class behavior may not be possible to infer effectively from a single record, but may be only be inferred by an aggregate set of records. Therefore, in this problem, the class label is associated with a set of instances both in the training and test data. Therefore, the problem may be understood to be that of classifying a set of data sets. Typically, the classification behavior may only be inferred from the overall patterns of data distribution, and very little information is embedded in any given record for classification purposes. We refer to this problem as the setwise classification problem.The problem can be extremely challenging in scenarios where the data is received in the form of a stream, and the records within any particular data set may not necessarily be received contiguously. In this paper, we present a first approach for real time and streaming classification of such data. We present experimental results illustrating the effectiveness of the approach.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {432–441},
numpages = {10},
keywords = {data streams, data classification},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623669,
author = {Ting, Daniel},
title = {Streamed Approximate Counting of Distinct Elements: Beating Optimal Batch Methods},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623669},
doi = {10.1145/2623330.2623669},
abstract = {Counting the number of distinct elements in a large dataset is a common task in web applications and databases. This problem is difficult in limited memory settings where storing a large hash table table is intractable. This paper advances the state of the art in probabilistic methods for estimating the number of distinct elements in a streaming setting New streaming algorithms are given that provably beat the "optimal" errors for Min-count and HyperLogLog while using the same sketch.This paper also contributes to the understanding and theory of probabilistic cardinality estimation introducing the concept of an area cutting process and the martingale estimator. These ideas lead to theoretical analyses of both old and new sketches and estimators and show the new estimators are optimal for several streaming settings while also providing accurate error bounds that match those obtained via simulation. Furthermore, the area cutting process provides a geometric intuition behind all methods for counting distinct elements which are not affected by duplicates. This intuition leads to a new sketch, Discrete Max-count, and the analysis of a class of sketches, self-similar area cutting decompositions that have attractive properties and unbiased estimators for both streaming and non-streaming settings.Together, these contributions lead to multi-faceted advances in sketch construction, cardinality and error estimation, the theory, and intuition for the problem of approximate counting of distinct elements for both the streaming and non-streaming cases.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {442–451},
numpages = {10},
keywords = {martingale, distinct elements, randomized algorithms, cardinality estimation},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623631,
author = {Lan, Andrew S. and Studer, Christoph and Baraniuk, Richard G.},
title = {Time-Varying Learning and Content Analytics via Sparse Factor Analysis},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623631},
doi = {10.1145/2623330.2623631},
abstract = {We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for educational applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA) that jointly traces learner concept knowledge over time, analyzes learner concept knowledge state transitions (induced by interacting with learning resources, such as textbook sections, lecture videos, etc., or the forgetting effect), and estimates the content organization and difficulty of the questions in assessments. These quantities are estimated solely from binary-valued (correct/incorrect) graded learner response data and the specific actions each learner performs (e.g., answering a question or studying a learning resource) at each time instant. Experimental results on two online course datasets demonstrate that SPARFA-Trace is capable of tracing each learner's concept knowledge evolution over time, analyzing the quality and content organization of learning resources, and estimating the question--concept associations and the question difficulties. Moreover, we show that SPARFA-Trace achieves comparable or better performance in predicting unobserved learner responses compared to existing collaborative filtering and knowledge tracing methods.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {452–461},
numpages = {10},
keywords = {expectation maximization, kalman filter, learning analytics, personalized learning, sparse factor analysis},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623673,
author = {Kushnir, Dan},
title = {Active-Transductive Learning with Label-Adapted Kernels},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623673},
doi = {10.1145/2623330.2623673},
abstract = {This paper presents an efficient active-transductive approach for classification. A common approach of active learning algorithms is to focus on querying points near the class boundary in order to refine it. However, for certain data distributions, this approach has been shown to lead to uninformative samples. More recent approaches consider combining data exploration with traditional refinement techniques. These techniques typically require tuning sampling of unexplored regions with refinement of detected class boundaries. They also involve significant computational costs for the exploration of informative query candidates. We present a novel iterative active learning algorithm designed to overcome these shortcomings by using a linear running-time active-transductive learning approach that naturally switches from exploration to refinement. The passive classifier employed in our algorithm builds a random-walk on the data graph based on a modified graph geometry that combines the data distribution with current label hypothesis; while the query component uses the uncertainty of the evolving hypothesis. Our supporting theory draws the link between the spectral properties of our iteration matrix and a solution to the minimal-cut problem for a fused hypothesis-data graph. Experiments demonstrate computational complexity that is orders of magnitude lower than state-of-the-art, and competitive results on benchmark data and real churn prediction data.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {462–471},
numpages = {10},
keywords = {active learning, transductive learning, uncertainty sampling, classification, graph},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623759,
author = {Vasisht, Deepak and Damianou, Andreas and Varma, Manik and Kapoor, Ashish},
title = {Active Learning for Sparse Bayesian Multilabel Classification},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623759},
doi = {10.1145/2623330.2623759},
abstract = {We study the problem of active learning for multilabel classification. We focus on the real-world scenario where the average number of positive (relevant) labels per data point is small leading to positive label sparsity. Carrying out mutual information based near-optimal active learning in this setting is a challenging task since the computational complexity involved is exponential in the total number of labels. We propose a novel inference algorithm for the sparse Bayesian multilabel model of [17]. The benefit of this alternate inference scheme is that it enables a natural approximation of the mutual information objective. We prove that the approximation leads to an identical solution to the exact optimization problem but at a fraction of the optimization cost. This allows us to carry out efficient, non-myopic, and near-optimal active learning for sparse multilabel classification. Extensive experiments reveal the effectiveness of the method.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {472–481},
numpages = {10},
keywords = {active learning, mutual information, multi-label learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623731,
author = {Wang, De and Nie, Feiping and Huang, Heng},
title = {Large-Scale Adaptive Semi-Supervised Learning via Unified Inductive and Transductive Model},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623731},
doi = {10.1145/2623330.2623731},
abstract = {Most semi-supervised learning models propagate the labels over the Laplacian graph, where the graph should be built beforehand. However, the computational cost of constructing the Laplacian graph matrix is very high. On the other hand, when we do classification, data points lying around the decision boundary (boundary points) are noisy for learning the correct classifier and deteriorate the classification performance. To address these two challenges, in this paper, we propose an adaptive semi-supervised learning model. Different from previous semi-supervised learning approaches, our new model needn't construct the graph Laplacian matrix. Thus, our method avoids the huge computational cost required by previous methods, and achieves a computational complexity linear to the number of data points. Therefore, our method is scalable to large-scale data. Moreover, the proposed model adaptively suppresses the weights of boundary points, such that our new model is robust to the boundary points. An efficient algorithm is derived to alternatively optimize the model parameter and class probability distribution of the unlabeled data, such that the induction of classifier and the transduction of labels are adaptively unified into one framework. Extensive experimental results on six real-world data sets show that the proposed semi-supervised learning model outperforms other related methods in most cases.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {482–491},
numpages = {10},
keywords = {large-scale semi-supervised learning, unified inductive and transductive model, semi-supervised learning},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623760,
author = {Gadde, Akshay and Anis, Aamir and Ortega, Antonio},
title = {Active Semi-Supervised Learning Using Sampling Theory for Graph Signals},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623760},
doi = {10.1145/2623330.2623760},
abstract = {We consider the problem of offline, pool-based active semi-supervised learning on graphs. This problem is important when the labeled data is scarce and expensive whereas unlabeled data is easily available. The data points are represented by the vertices of an undirected graph with the similarity between them captured by the edge weights. Given a target number of nodes to label, the goal is to choose those nodes that are most informative and then predict the unknown labels. We propose a novel framework for this problem based on our recent results on sampling theory for graph signals. A graph signal is a real-valued function defined on each node of the graph. A notion of frequency for such signals can be defined using the spectrum of the graph Laplacian matrix. The sampling theory for graph signals aims to extend the traditional Nyquist-Shannon sampling theory by allowing us to identify the class of graph signals that can be reconstructed from their values on a subset of vertices. This approach allows us to define a criterion for active learning based on sampling set selection which aims at maximizing the frequency of the signals that can be reconstructed from their samples on the set. Experiments show the effectiveness of our method.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {492–501},
numpages = {10},
keywords = {sampling theory, active semi-supervised learning, graph signal filtering, graph signal processing},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623730,
author = {Wang, Jialei and Srebro, Nathan and Evans, James},
title = {Active Collaborative Permutation Learning},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623730},
doi = {10.1145/2623330.2623730},
abstract = {We consider the problem of Collaborative Permutation Recovery, i.e. recovering multiple permutations over objects (e.g. preference rankings over different options) from limited pairwise comparisons. We tackle both the problem of how to recover multiple related permutations from limited observations, and the active learning problem of which pairwise comparison queries to ask so as to allow better recovery. There has been much work on recovering single permutations from pairwise comparisons, but we show that considering several related permutations jointly we can leverage their relatedness so as to reduce the number of comparisons needed compared to reconstructing each permutation separately. To do so, we take a collaborative filtering / matrix completion approach and use a trace-norm or max-norm regularized matrix learning model. Our approach can also be seen as a collaborative learning version of Jamieson and Nowak's recent work on constrained permutation recovery, where instead of basing the recovery on known features, we learn the best features de novo.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {502–511},
numpages = {10},
keywords = {active learning, matrix factorization, collaborative ranking},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623611,
author = {Nguyen, Xuan Vinh and Chan, Jeffrey and Romano, Simone and Bailey, James},
title = {Effective Global Approaches for Mutual Information Based Feature Selection},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623611},
doi = {10.1145/2623330.2623611},
abstract = {Most current mutual information (MI) based feature selection techniques are greedy in nature thus are prone to sub-optimal decisions. Potential performance improvements could be gained by systematically posing MI-based feature selection as a global optimization problem. A rare attempt at providing a global solution for the MI-based feature selection is the recently proposed Quadratic Programming Feature Selection (QPFS) approach. We point out that the QPFS formulation faces several non-trivial issues, in particular, how to properly treat feature `self-redundancy' while ensuring the convexity of the objective function. In this paper, we take a systematic approach to the problem of global MI-based feature selection. We show how the resulting NP-hard global optimization problem could be efficiently approximately solved via spectral relaxation and semi-definite programming techniques. We experimentally demonstrate the efficiency and effectiveness of these novel feature selection frameworks.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {512–521},
numpages = {10},
keywords = {feature selection, global optimization, spectral relaxation, semi-definite programming, mutual information},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623635,
author = {Xu, Zhixiang and Huang, Gao and Weinberger, Kilian Q. and Zheng, Alice X.},
title = {Gradient Boosted Feature Selection},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623635},
doi = {10.1145/2623330.2623635},
abstract = {A feature selection algorithm should ideally satisfy four conditions: reliably extract relevant features; be able to identify non-linear feature interactions; scale linearly with the number of features and dimensions; allow the incorporation of known sparsity structure. In this work we propose a novel feature selection algorithm, Gradient Boosted Feature Selection (GBFS), which satisfies all four of these requirements. The algorithm is flexible, scalable, and surprisingly straight-forward to implement as it is based on a modification of Gradient Boosted Trees. We evaluate GBFS on several real world data sets and show that it matches or outperforms other state of the art feature selection algorithms. Yet it scales to larger data set sizes and naturally allows for domain-specific side information.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {522–531},
numpages = {10},
keywords = {large-scale, gradient boosting, feature selection},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623662,
author = {Xiang, Shuo and Yang, Tao and Ye, Jieping},
title = {Simultaneous Feature and Feature Group Selection through Hard Thresholding},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623662},
doi = {10.1145/2623330.2623662},
abstract = {Selecting an informative subset of features has important applications in many data mining tasks especially for high-dimensional data. Recently, simultaneous selection of features and feature groups (a.k.a bi-level selection) becomes increasingly popular since it not only reduces the number of features but also unveils the underlying grouping effect in the data, which is a valuable functionality in many applications such as bioinformatics and web data mining. One major challenge of bi-level selection (or even feature selection only) is that computing a globally optimal solution requires a prohibitive computational cost. To overcome such a challenge, current research mainly falls into two categories. The first one focuses on finding suitable continuous computational surrogates for the discrete functions and this leads to various convex and nonconvex optimization models. Although efficient, convex models usually deliver sub-optimal performance while nonconvex models on the other hand require significantly more computational effort. Another direction is to use greedy algorithms to solve the discrete optimization directly. However, existing algorithms are proposed to handle single-level selection only and it remains challenging to extend these methods to handle bi-level selection. In this paper, we fulfill this gap by introducing an efficient sparse group hard thresholding algorithm. Our main contributions are: (1) we propose a novel bi-level selection model and show that the key combinatorial problem admits a globally optimal solution using dynamic programming; (2) we provide an error bound between our solution and the globally optimal under the RIP (Restricted Isometry Property) theoretical framework. Our experiments on synthetic and real data demonstrate that the proposed algorithm produces encouraging performance while keeping comparable computational efficiency to convex relaxation models.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {532–541},
numpages = {10},
keywords = {optimization, com- binatorics, supervised learning, dynamic programming, bi-level learning, feature selection},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623686,
author = {Zhao, Zheng and Liu, Jun and Cox, James},
title = {Safe and Efficient Screening for Sparse Support Vector Machine},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623686},
doi = {10.1145/2623330.2623686},
abstract = {Sparse support vector machine (SVM) is a robust predictive model that can effectively remove noise and preserve signals. Like Lasso, it can efficiently learn a solution path based on a set of predefined parameters and therefore provides strong support for model selection. Sparse SVM has been successfully applied in a variety of data mining applications including text mining, bioinformatics, and image processing. The emergence of big-data analysis poses new challenges for model selection with large-scale data that consist of tens of millions samples and features. In this paper, a novel screening technique is proposed to accelerate model selection for l1-regularized l2-SVM and effectively improve its scalability. This technique can precisely identify inactive features in the optimal solution of a l1-regularized l2-SVM model and remove them before training. The technique makes use of the variational inequality and provides a closed-form solution for screening inactive features in different situations. Every feature that is removed by the screening technique is guaranteed to be inactive in the optimal solution. Therefore, when l1-regularized l2-SVM uses the features selected by the technique, it achieves exactly the same result as when it uses the full feature set. Because the technique can remove a large number of inactive features, it can greatly increase the efficiency of model selection for l1-regularized l2-SVM. Experimental results on five high-dimensional benchmark data sets demonstrate the power of the proposed technique.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {542–551},
numpages = {10},
keywords = {screening, sparse support vector machine, feature selection},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623747,
author = {Purushotham, Sanjay and Min, Martin Renqiang and Kuo, C.-C. Jay and Ostroff, Rachel},
title = {Factorized Sparse Learning Models with Interpretable High Order Feature Interactions},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623747},
doi = {10.1145/2623330.2623747},
abstract = {Identifying interpretable discriminative high-order feature interactions given limited training data in high dimensions is challenging in both machine learning and data mining. In this paper, we propose a factorization based sparse learning framework termed FHIM for identifying high-order feature interactions in linear and logistic regression models, and study several optimization methods for solving them. Unlike previous sparse learning methods, our model FHIM recovers both the main effects and the interaction terms accurately without imposing tree-structured hierarchical constraints. Furthermore, we show that FHIM has oracle properties when extended to generalized linear regression models with pairwise interactions. Experiments on simulated data show that FHIM outperforms the state-of-the-art sparse lear-ning techniques. Further experiments on our experimentally generated data from patient blood samples using a novel SOMAmer (Slow Off-rate Modified Aptamer) technology show that, FHIM performs blood-based cancer diagnosis and bio-marker discovery for Renal Cell Carcinoma much better than other competing methods, and it identifies interpretable block-wise high-order gene interactions predictive of cancer stages of samples. A literature survey shows that the interactions identified by FHIM play important roles in cancer development.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {552–561},
numpages = {10},
keywords = {sparse learning, feature selection},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623708,
author = {Cheng, Dehua and Liu, Yan},
title = {Parallel Gibbs Sampling for Hierarchical Dirichlet Processes via Gamma Processes Equivalence},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623708},
doi = {10.1145/2623330.2623708},
abstract = {The hierarchical Dirichlet process (HDP) is an intuitive and elegant technique to model data with latent groups. However, it has not been widely used for practical applications due to the high computational costs associated with inference. In this paper, we propose an effective parallel Gibbs sampling algorithm for HDP by exploring its connections with the gamma-gamma-Poisson process. Specifically, we develop a novel framework that combines bootstrap and Reversible Jump MCMC algorithm to enable parallel variable updates. We also provide theoretical convergence analysis based on Gibbs sampling with asynchronous variable updates. Experiment results on both synthetic datasets and two large-scale text collections show that our algorithm can achieve considerable speedup as well as better inference accuracy for HDP compared with existing parallel sampling algorithms.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {562–571},
numpages = {10},
keywords = {hierarchical dirichlet process, parallel inference, topic model},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623716,
author = {Dasu, Tamraparni and Loh, Ji Meng and Srivastava, Divesh},
title = {Empirical Glitch Explanations},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623716},
doi = {10.1145/2623330.2623716},
abstract = {Data glitches are unusual observations that do not conform to data quality expectations, be they logical, semantic or statistical. By applying data integrity constraints, potentially large sections of data could be flagged as being noncompliant. Ignoring or repairing significant sections of the data could fundamentally bias the results and conclusions drawn from analyses. In the context of Big Data where large numbers and volumes of feeds from disparate sources are integrated, it is likely that significant portions of seemingly noncompliant data are actually legitimate usable data.In this paper, we introduce the notion of Empirical Glitch Explanations - concise, multi-dimensional descriptions of subsets of potentially dirty data - and propose a scalable method for empirically generating such explanatory characterizations. The explanations could serve two valuable functions: (1) Provide a way of identifying legitimate data and releasing it back into the pool of clean data. In doing so, we reduce cleaning-related statistical distortion of the data; (2) Used to refine existing data quality constraints and generate and formalize domain knowledge.We conduct experiments using real and simulated data to demonstrate the scalability of our method and the robustness of explanations. In addition, we use two real world examples to demonstrate the utility of the explanations where we reclaim over 99% of the suspicious data, keeping data repair related statistical distortion close to 0.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {572–581},
numpages = {10},
keywords = {data quality, quantitative data cleaning, glitch explanations, crossover subsampling},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623727,
author = {Yang, Hongxia and He, Jingrui},
title = {Learning with Dual Heterogeneity: A Nonparametric Bayes Model},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623727},
doi = {10.1145/2623330.2623727},
abstract = {Traditional data mining techniques are designed to model a single type of heterogeneity, such as multi-task learning for modeling task heterogeneity, multi-view learning for modeling view heterogeneity, etc. Recently, a variety of real applications emerged, which exhibit dual heterogeneity, namely both task heterogeneity and view heterogeneity. Examples include insider threat detection across multiple organizations, web image classification in different domains, etc. Existing methods for addressing such problems typically assume that multiple tasks are equally related and multiple views are equally consistent, which limits their application in complex settings with varying task relatedness and view consistency. In this paper, we advance state-of-the-art techniques by adaptively modeling task relatedness and view consistency via a nonparametric Bayes model: we model task relatedness using normal penalty with sparse covariances, and view consistency using matrix Dirichlet process. Based on this model, we propose the NOBLE algorithm using an efficient Gibbs sampler. Experimental results on multiple real data sets demonstrate the effectiveness of the proposed algorithm.},
booktitle = {Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
pages = {582–590},
numpages = {9},
keywords = {Gibbs sampler, nonparametric Bayes modeling, multi-task multi-view},
location = {New York, New York, USA},
series = {KDD '14}
}

@inproceedings{10.1145/2623330.2623636,
author = {Liu, Chien-Liang and Tsai, Tsung-Hsun and Lee, Chia-Hoang},
title = {Online Chinese Restaurant Process},
year = {2014},
isbn = {9781450329569},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2623330.2623636},
doi = {10.1145/2623330.2623636},
abstract = {Processing large volumes of streaming data in near-real-time is becoming increasingly important as the Internet, sensor networks and network traffic grow. Online machine learning is a typical means of dealing with streaming data, since it allows the classification model to learn one instance of data at a time. Although many online learning methods have been developed since the development of the Perceptron algorithm, existing online methods assume that the number of classes is available in advance of classification process. However, this assumption is unrealistic for large scale or streaming data sets. This work proposes an online Chinese restaurant process (CRP) algorithm, which is an online and nonparametric algorithm, to

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