ICML 2010 - Accepted Papers

Invited Application Track
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• 901: Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine
  Thore Graepel (Microsoft Research); Joaquin Quiñonero Candela (Microsoft Research); Thomas Borchert (Microsoft Research); Ralf Herbrich (Microsoft Research)
  
  
• 902: Detecting Large-Scale System Problems by Mining Console Logs
  Wei Xu (UC Berkeley); Ling Huang (Intel Labs Berkeley); Armando Fox (UC Berkeley); David Patterson (UC Berkeley); Michael I. Jordan (UC Berkeley)
  
  
• 903: The Role of Machine Learning in Business Optimization
  Chid Apte (IBM T. J. Watson Research Center)
  
  
• 904: Music Plus One and Machine Learning
  Christopher Raphael (Indiana University)
  
  
• 905: Climbing the Tower of Babel: Unsupervised Multilingual Learning
  Benjamin Snyder (MIT); Regina Barzilay (MIT)
  
  
• 906:FAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned Visual Vocabulary Model
  Mark Cummins (University of Oxford); Paul Newman (University of Oxford)
  
  
• 907: Discriminative Latent Variable Models for Object Detection
  Pedro Felzenszwalb (University of Chicago); Ross Girshick (University of Chicago); David McAllester (Toyota Technological Institute at Chicago); Deva Ramanan (UC Irvine)
  
  

Main Track
Jump to Invited Application Track

The following 152 papers have been accepted:

• 16: Large Graph Construction for Scalable Semi-Supervised Learning
  Wei Liu (Columbia University); Junfeng He; Shih-Fu Chang
  
  
• 23: Boosting Classifiers with Tightened L0-Relaxation Penalties
  Noam Goldberg (Technion); Jonathan Eckstein
  
  
• 26: Variable Selection in Model-Based Clustering: To Do or To Facilitate
  Leonard Poon (HKUST); Nevin Zhang (Hong Kong University Of Science & Technology); Tao Chen; Yi Wang
  
  
• 28: Modeling Interaction via the Principle of Maximum Causal Entropy
  Brian Ziebart (Carnegie Mellon University); Drew Bagnell (Cmu); Anind Dey (Carnegie Mellon University)
  
  
• 35: Multi-Task Learning of Gaussian Graphical Models
  Jean Honorio (Stony Brook University); Dimitris Samaras (Stony Brook University)
  
  
• 45: Spherical Topic Models
  Joseph Reisinger (UT Austin); Austin Waters (UT Austin); Bryan Silverthorn (UT Austin); Raymond Mooney (University Of Texas At Austin)
  
  
• 52: Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
  Marek Petrik (University of Massachusetts ); Gavin Taylor (Duke); Ron Parr (Duke); Shlomo Zilberstein (University of Massachusetts Amherst)
  
  
• 76: Multi-agent Learning Experiments on Repeated Matrix Games
  Bruno Bouzy (University Paris Descartes); Marc M?tivier (University Paris Descartes)
  
  
• 77: Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds
  Tobias Lang (TU Berlin); Marc Toussaint (Tu Berlin)
  
  
• 78: Causal filter selection in microarray data
  Gianluca Bontempi (University Libre De Bruxelles); Patrick Meyer (ULB)
  
  
• 87: A Conditional Random Field for Multi-Instance Learning
  Thomas Deselaers (ETH Zurich); Vittorio Ferrari (ETH Zurich)
  
  
• 99: Supervised Aggregation of Classifiers using Artificial Prediction Markets
  Nathan Lay (Florida State University); Adrian Barbu (Florida State University)
  
  
• 100: 3D Convolutional Neural Networks for Human Action Recognition
  Shuiwang Ji (Arizona State University); Wei Xu; Ming Yang; Kai Yu (NEC Research Princeton)
  
  
• 107: Asymptotic Analysis of Generative Semi-Supervised Learning
  Joshua Dillon (Georgia Institute of Technolog); Krishnakumar Balasubramanian (Georgia Institute of Technology); Guy Lebanon (Georgia Tech)
  
  
• 115: Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate
  Phil Long (Google); Rocco Servedio (Columbia)
  
  
• 117: Learning from Noisy Side Information by Generalized Maximum Entropy Model
  Tianbao Yang (Michigan State University); Rong Jin (Michigan State University); Anil Jain (michigan State University)
  
  
• 119: Finding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering
  Nader Bshouty (Technion); Phil Long (Google)
  
  
• 123: The Elastic Embedding Algorithm for Dimensionality Reduction
  Miguel Carreira-Perpinan (UC Merced)
  
  
• 125: Two-Stage Learning Kernel Algorithms
  Corinna Cortes (Google); Mehryar Mohri (Courant Institute Nyu); Afshin Rostamizadeh (Courant Institute, NYU)
  
  
• 132: Robust Graph Mode Seeking by Graph Shift
  Hairong Liu (NUS); Shuicheng Yan (National University of Singapore)
  
  
• 137: Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning
  Matan Gavish (Stanford University); Boaz Nadler (Weizmann Institute Of Science); Ronald Coifman (Yale University)
  
  
• 149: Deep Supervised T-Distributed Embedding
  Renqiang Min (DCS, University of Toronto); Laurens van der Maaten (Tilburg University); Zineng Yuan (University of Toronto); Anthony Bonner (University of Toronto); Zhaolei Zhang (University of Toronto)
  
  
• 168: A Nonparametric Information Theoretic Clustering Algorithm
  Lev Faivishevsky (Bar Ilan University); Jacob Goldberger (Bar Ilan University)
  
  
• 170: Gaussian Process Change Point Models
  Yunus Saatci (University of Cambridge); Ryan Turner; Carl Rasmussen
  
  
• 175: Dynamical Products of Experts for Modeling Financial Time Series
  Yutian Chen (Univ. of California, Irvine); Max Welling (University Of California - Irvine)
  
  
• 176: The Margin Perceptron with Unlearning
  Constantinos Panagiotakopoulos (Aristotle University of Thessaloniki); Petroula Tsampouka (Aristotle University of Thessaloniki)
  
  
• 178: Sequential Projection Learning for Hashing with Compact Codes
  Jun Wang (Columbia University); Sanjiv Kumar (Google Research Ny); Shih-Fu Chang
  
  
• 179: Generalization Bounds for Learning Kernels
  Corinna Cortes (Google); Mehryar Mohri (Courant Institute Nyu); Afshin Rostamizadeh (Courant Institute, NYU)
  
  
• 180: Modeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process
  Kevin Canini (University of California); Mikhail Shashkov (Univeristy of California, Berkeley); Tom Griffiths (Berkeley)
  
  
• 187: Convergence of Least Squares Temporal Difference Methods Under General Conditions
  Huizhen Yu (Univ. of Helsinki)
  
  
• 191: Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration
  Michael Wunder (Rutgers University); Michael Littman (Rutgers University); Monica Babes (Rutgers)
  
  
• 195: Estimation of (near) low-rank matrices with noise and high-dimensional scaling
  Sahand Negahban (UC Berkeley); Martin Wainwright (UC Berkeley)
  
  
• 196: A Simple Algorithm for Nuclear Norm Regularized Problems
  Martin Jaggi (ETH Z?rich); Marek Sulovsk? (ETH Z?rich, Switzerland)
  
  
• 197: On Sparse Nonparametric Conditional Covariance Selection
  Mladen Kolar (Carnegie Mellon University); Ankur Parikh (Carnegie Mellon University); Eric Xing (Cmu)
  
  
• 202: Exploiting Data-Independence for Fast Belief-Propagation
  Julian McAuley (NICTA); Tiberio Caetano (Nicta)
  
  
• 207: One-sided Support Vector Regression for Multiclass Cost-sensitive Classification
  Han-Hsing Tu (National Taiwan University); Hsuan-Tien Lin (National Taiwan University)
  
  
• 219: OTL: A Framework of Online Transfer Learning
  Peilin Zhao (Nanyang Technological University); Steven C.H. Hoi (Nanyang Technological Universi)
  
  
• 223: SVM Classifier Estimation from Group Probabilities
  Stefan Rueping (Fraunhofer IAIS)
  
  
• 227: Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets
  Mingkui Tan (NTU, Singapore); Li Wang (University of California, San Diego); Ivor Tsang (NTU)
  
  
• 233: Total Variation and Cheeger Cuts
  Arthur Szlam (NYU); Xavier Bresson (UCLA)
  
  
• 235: Learning Temporal Graphs for Relational Time-Series Analysis
  Yan Liu (IBM TJ Watson Research Center); ALEXANDRU NICULESCU-MIZIL; AURELIE LOZANO (IBM TJ Watson Research Center); Yong Lu (Havard University)
  
  
• 238: Online Streaming Feature Selection
  Xindong Wu (University of Vermont); Kui Yu (Hefei Univerity of Technology); Hao Wang (Hefei Univerity of Technology); Wei Ding (University of Massachusetts Boston)
  
  
• 242: Making Large-Scale Nystrom Approximation Possible
  Mu Li (Shanghai Jiao Tong University); James Kwok (University Of Science And Technology Hong Kong); Bao-Liang Lu (SJTU)
  
  
• 246: Particle Filtered MCMC-MLE with Connections to Contrastive Divergence
  Arthur Asuncion (UC Irvine); Qiang Liu (UC Irvine); Alex Ihler (UC Irvine); Padhraic Smyth (UC Irvine)
  
  
• 247: Feature Selection as a One-Player Game
  Romaric Gaudel (Lri); Michele Sebag (Universite Paris Sud)
  
  
• 248: The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data
  Julia Vogt (University of Basel); Sandhya Prabhakaran (University of Basel); Thomas Fuchs (ETH Zurich); Volker Roth (Eth Zurich)
  
  
• 259: Online Prediction with Privacy
  Jun Sakuma (University of Tsukuba ); Hiromi Arai (University of Tsukuba)
  
  
• 263: Fast boosting using adversarial bandits
  R?bert Busa-Fekete (CNRS / University Paris-Sud 11); Balazs Kegl (LAL/LRI Universite Paris-Sud, CNRS)
  
  
• 268: Robust Formulations for Handling Uncertainty in Kernel Matrices
  Sahely Bhadra (Indian Institute of Science.); Sourangshu Bhattacharya (Yahoo! Research Labs, Bangalore,INDIA); Chiranjib Bhattacharyya (Indian Institute Of Science); Aharon Ben-Tal (Technion-Israel Institute of T)
  
  
• 269: Bayesian Multi-Task Reinforcement Learning
  Alessandro Lazaric (Inria); Mohammad Ghavamzadeh (Inria)
  
  
• 275: A New Analysis of Co-Training
  Wei Wang (Nanjing University); Zhi-Hua Zhou (Nanjing University)
  
  
• 279: Clustering processes
  Daniil Ryabko (INRIA)
  
  
• 280: COFFIN : A Computational Framework for Linear SVMs
  Soeren Sonnenburg (Tu Berlin); Vojtech Franc (Czech Technical University)
  
  
• 284: Multiagent Inductive Learning: an Argumentation-based Approach
  Santiago Ontanon (IIIA-CSIC); Enric Plaza (Iiia)
  
  
• 285: Active Risk Estimation
  Christoph Sawade (University of Potsdam); Niels Landwehr (University Of Potsdam); Steffen Bickel (Nokia gate5 GmbH); Tobias Scheffer (University of Potsdam)
  
  
• 286: Heterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-Time Segment Information Sharing
  Frank Dondelinger (BioSS); Sophie Lebre (University of Strasbourg); Dirk Husmeier (BioSS)
  
  
• 295: Temporal Difference Bayesian Model Averaging: A Bayesian Perspective on Adapting Lambda
  Carlton Downey (Victoria University of Wellington); Scott Sanner (Nicta)
  
  
• 297: Surrogating the surrogate: accelerating Gaussian-process-based global optimization with a mixture cross-entropy algorithm
  R?mi Bardenet (LRI); Balazs Kegl (LAL/LRI Universite Paris-Sud, CNRS)
  
  
• 298: Random Spanning Trees and the Prediction of Weighted Graphs
  Nicolo Cesa-Bianchi (University Of Milan); Claudio Gentile (Universita' Dell'Insubria); Fabio Vitale (Universita' di Milano); Giovanni Zappella (Universita' di Milano)
  
  
• 303: Analysis of a Classification-based Policy Iteration Algorithm
  Alessandro Lazaric (Inria); Mohammad Ghavamzadeh (Inria); Remi Munos (Inria)
  
  
• 310: Unsupervised Risk Stratification in Clinical Datasets: Identifying Patients at Risk of Rare Outcomes
  Zeeshan Syed (University of Michigan); Ilan Rubinfeld (Henry Ford Health System)
  
  
• 311: Gaussian Covariance and Scalable Variational Inference
  Matthias Seeger (Saarland University And Max Planck Institute For Informatics)
  
  
• 319: Efficient Learning with Partially Observed Attributes
  Nicolo Cesa-Bianchi (University Of Milan); Shai Shalev-Shwartz (Hebrew University); Ohad Shamir (Huji)
  
  
• 330: Boosting for Regression Transfer
  David Pardoe (University of Texas at Austin); Peter Stone (University Of Texas At Austin)
  
  
• 331: Label Ranking under Ambiguous Supervision for Learning Semantic Correspondences
  Antoine Bordes (Lip6 Paris); Nicolas Usunier ( LIP6, University Pierre et Marie Curie - Paris 6); Jason Weston (Google Nyc)
  
  
• 333: From Transformation-Based Dimensionality Reduction to Feature Selection
  Mahdokht Masaeli (Northeastern University); Glenn Fung; Jennifer Dy (Northeastern University)
  
  
• 336: Least-Squares ? Policy Iteration: Bias-Variance Trade-off in Control Problems
  Christophe Thiery (Loria); Bruno Scherrer (Loria)
  
  
• 342: Multiple Non-Redundant Spectral Clustering Views
  Donglin Niu (Northeastern University); Jennifer Dy (Northeastern University); Michael Jordan (UC Berkeley)
  
  
• 344: Large Scale Max-Margin Multi-Label Classification with Priors
  Bharath Hariharan (Indian Institute of Technology Delhi); Lihi Zelnik-Manor (Technion); S.V.N. Vishwanathan (Purdue); Manik Varma (Microsoft Research India)
  
  
• 347: Fast Neighborhood Subgraph Pairwise Distance Kernel
  Fabrizio Costa (ku leuven); Kurt De Grave (K.U.Leuven)
  
  
• 352: Tree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity
  Seyoung Kim (Carnegie Mellon University); Eric Xing (Cmu)
  
  
• 353: Label Ranking Methods based on the Plackett-Luce Model
  Weiwei Cheng (University Marburg); Krzysztof Dembczynski (Department Of Mathematics And Computer Science University Of Marburg Germany); Eyke Huellermeier (University of Marburg)
  
  
• 359: A DC Programming Approach for Sparse Eigenvalue Problem
  Mamadou THIAO (LMI INSA of Rouen-France); Tao PHAM DINH (LMI INSA of Rouen-France); Hoai An LE THI (LITA - UFR MIM, Metz University Metz-France)
  
  
• 366: Submodular Dictionary Selection for Sparse Representation
  Andreas Krause (Caltech); Volkan Cevher (EPFL)
  
  
• 370: Deep networks for robust visual recognition
  Yichuan Tang (University of Waterloo); Chris Eliasmith (University of Waterloo)
  
  
• 371: A Stick-Breaking Construction of the Beta Process
  John Paisley (Duke University); Aimee Zaas (Duke Medical Center); Christopher Woods (Duke Medical Center); Geoffrey Ginsburg (Duke Medical Center); Lawrence Carin (Duke University)
  
  
• 374: Local Minima Embedding
  Minyoung Kim (CMU); Fernando De la Torre (CMU)
  
  
• 376: Risk minimization, probability elicitation, and cost-sensitive SVMs
  Hamed Masnadi-Shirazi (UCSD); nuno Vasconcelos (University of California at San Diego)
  
  
• 378: Continuous-Time Belief Propagation
  Tal El-Hay (Hebrew University); Ido Cohn; Nir Friedman (Hebrew University); Raz Kupferman (Hebrew University)
  
  
• 384: A Language-based Approach to Measuring Scholarly Impact
  Sean Gerrish (Princeton University); David Blei (Princeton University)
  
  
• 387: Power Iteration Clustering
  Frank Lin (Carnegie Mellon University); William Cohen (Cmu)
  
  
• 397: The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling
  Sinead Williamson (University Of Cambridge); Chong Wang (Princeton); Katherine Heller (Gatsby Computational Neuroscience Unit); David Blei (Princeton University)
  
  
• 406: Budgeted Distribution Learning of Belief Net Parameters
  Barnabas Poczos (University of Alberta); Liuyang Li; Csaba Szepesvari (University Of Alberta); Russell Greiner (University of Alberta)
  
  
• 410: Efficient Selection of Multiple Bandit Arms: Theory and Practice
  Shivaram Kalyanakrishnan (University of Texas at Austin); Peter Stone (University Of Texas At Austin)
  
  
• 412: Gaussian Processes Multiple Instance Learning
  Minyoung Kim (CMU); Fernando De la Torre (CMU)
  
  
• 416: Proximal Methods for Sparse Hierarchical Dictionary Learning
  Rodolphe Jenatton (Inria); Julien Mairal (Inria); Guillaume Obozinski (Inria); Francis Bach (INRIA)
  
  
• 420: Conditional Topic Random Fields
  Jun Zhu; Eric Xing (Cmu)
  
  
• 421: On the Consistency of Ranking Algorithms
  John Duchi (UC Berkeley); Lester Mackey (U.C. Berkeley); Michael Jordan (University Of California At Berkeley)
  
  
• 422: Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
  Niranjan Srinivas (California Institute of Technology); Andreas Krause (Caltech); Sham Kakade (Wharton); Matthias Seeger (Saarland University And Max Planck Institute For Informatics)
  
  
• 429: Implicit Online Learning
  Brian Kulis (University Of California At Berkeley); Peter Bartlett (UC Berkeley)
  
  
• 432: Rectified Linear Units Improve Restricted Boltzmann Machines
  Vinod Nair (University of Toronto); Geoffrey Hinton (University of Toronto)
  
  
• 433: Budgeted Nonparametric Learning from Data Streams
  Ryan Gomes (California Institute of Technology); Andreas Krause (Caltech)
  
  
• 436: Interactive Submodular Set Cover
  Andrew Guillory (University of Washington); Jeff Bilmes (University of Washington, Seat)
  
  
• 438: A fast natural Newton method
  Nicolas Le Roux (Microsoft Cambridge); Andrew Fitzgibbon (Microsoft Research)
  
  
• 441: Learning Deep Boltzmann Machines using Adaptive MCMC
  Ruslan Salakhutdinov (MIT)
  
  
• 442: Internal Rewards Mitigate Agent Boundedness
  Jonathan Sorg (University of Michigan); Satinder Singh (University of Michigan); Richard Lewis (University of Michigan)
  
  
• 446: Learning optimally diverse rankings over large document collections
  Aleksandrs Slivkins (Microsoft Research); Filip Radlinski (Microsoft Research); Sreenivas Gollapudi (Microsoft Research)
  
  
• 449: Learning Fast Approximations of Sparse Coding
  Karol Gregor (New York University); Yann LeCun (New York University)
  
  
• 451: Boosted Backpropagation Learning for Training Deep Modular Networks
  Alexander Grubb (Carnegie Mellon University); Drew Bagnell (Cmu)
  
  
• 453: Convergence, Targeted Optimality, and Safety in Multiagent Learning
  DORAN CHAKRABORTY (UNIVERSITY OF TEXAS, AUSTIN); Peter Stone (University Of Texas At Austin)
  
  
• 454: Improved Local Coordinate Coding using Local Tangents
  Kai Yu (NEC Research Princeton); Tong Zhang (Rutgers University)
  
  
• 458: Deep learning via Hessian-free optimization
  James Martens (University of Toronto)
  
  
• 464: Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis
  Daniel Lizotte (University of Michigan); Michael Bowling (University of Alberta); Susan Murphy (University of Michigan)
  
  
• 468: Cognitive Models of Test-Item Effects in Human Category Learning
  Xiaojin Zhu (University of Wisconsin, Madison); Bryan Gibson; Kwang-Sung Jun; Tim Rogers; Joseph Harrison; Chuck Kalish (University of Wisconsin-Madison)
  
  
• 473: Online Learning for Group Lasso
  Haiqin Yang (The Chinese University of HK); Zenglin Xu; Irwin King; Michael Lyu
  
  
• 475: Generalizing Apprenticeship Learning across Hypothesis Classes
  Thomas Walsh (Rutgers University); Kaushik Subramanian (Rutgers University); Michael Littman (Rutgers University); Carlos Diuk (Princeton University)
  
  
• 481: Projection Penalties: Dimension Reduction without Loss
  Yi Zhang (Carnegie Mellon University); Jeff Schneider (Carnegie Mellon University)
  
  
• 493: Application of Machine Learning To Epileptic Seizure Detection
  Ali Shoeb (Massachusetts Institute of Technology); John Guttag (Massachusetts Institute of Technology)
  
  
• 495: Hilbert Space Embeddings of Hidden Markov Models
  Le Song (Cmu); Byron Boots (Carnegie Mellon University); Sajid Siddiqi (Google); Geoffrey Gordon (Carnegie Mellon University); Alex Smola (Yahoo! Research)
  
  
• 502: Learning Markov Logic Networks Using Structural Motifs
  Stanley Kok (University of Washington); Pedro Domingos (University Of Washington)
  
  
• 504: Metric Learning to Rank
  Brian McFee (UC San Diego); Gert Lanckriet (Ucsd)
  
  
• 505: Transfer Learning for Collective Link Prediction in Multiple Heterogenous Domains
  Bin Cao (HKUST); Nathan Liu (HKUST); Qiang Yang
  
  
• 518: Implicit Regularization in Variational Bayesian Matrix Factorization
  Shinichi Nakajima (Nikon Corporation); Masashi Sugiyama (Tokyo Institute Of Technology)
  
  
• 520: On learning with kernels for unordered pairs
  Martial Hue (Mines Paristech); Jean-Philippe Vert (Mines ParisTech)
  
  
• 521: Robust Subspace Segmentation by Low-Rank Representation
  Guangcan Liu (SJTU); Zhouchen Lin (Visual Computing Group, Microsoft Research Asia); Yong Yu
  
  
• 522: Structured Output Learning with Indirect Supervision
  Ming-Wei Chang (University Of Illinois); Vivek Srikumar; Dan Goldwasser (University of Illinois); Dan Roth (University of Illinois at Urbana-Champaign)
  
  
• 523: Bayesian Nonparametric Matrix Factorization for Recorded Music
  Matthew Hoffman (Princeton University); David Blei (Princeton University); Perry Cook (Princeton University)
  
  
• 532: Learning the Linear Dynamical System with ASOS
  James Martens (University of Toronto)
  
  
• 537: Bottom-Up Learning of Markov Network Structure
  Jesse Davis (University Of Washington); Pedro Domingos (University Of Washington)
  
  
• 540: Simple and Efficient Multiple Kernel Learning By Group Lasso
  Zenglin Xu; Rong Jin (Michigan State University); Haiqin Yang (The Chinese University of HK); Irwin King; Michael Lyu
  
  
• 544: Active Learning for Networked Data
  Mustafa Bilgic (University of Maryland at CP); Lilyana Mihalkova (University Of Maryland); Lise Getoor (University Of Maryland)
  
  
• 546: Model-based reinforcement learning with nearly tight exploration complexity bounds
  Istvan Szita (University of Alberta); Csaba Szepesvari (University Of Alberta)
  
  
• 549: Forgetting Counts: Constant Memory Inference for a Dependent Hierarchical Pitman-Yor Process
  Nicholas Bartlett (Columbia University); David Pfau (Columbia University); Frank Wood (Columbia University)
  
  
• 551: Distance Dependent Chinese Restaurant Processes
  David Blei (Princeton University); Peter Frazier (Cornell)
  
  
• 553: Mixed Membership Matrix Factorization
  Lester Mackey (U.C. Berkeley); David Weiss (University of Pennsylvania); Michael Jordan (University Of California At Berkeley)
  
  
• 554: An Analysis of the Convergence of Graph Laplacians
  Daniel Ting (UC Berkeley); Ling Huang (Intel Labs Berkeley); Michael Jordan (University Of California At Berkeley)
  
  
• 556: A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices
  Ryota Tomioka (University of Tokyo); Taiji Suzuki (University of Tokyo); Masashi Sugiyama (Tokyo Institute Of Technology); Hisashi Kashima (University of Tokyo)
  
  
• 562: A scalable trust-region algorithm with application to mixed-norm regression
  Dongmin Kim (University of Texas at Austin); Suvrit Sra (Mpi For Biological Cybernetics); Inderjit Dhillon (University of Texas at Austin)
  
  
• 568: Learning Programs: A Hierarchical Bayesian Approach
  Percy Liang (University Of California - Berkeley); Michael Jordan (University Of California At Berkeley); Dan Klein (UC Berkeley)
  
  
• 569: Multi-Class Pegasos on a Budget
  Zhuang Wang (Temple University); Koby Crammer (Department Of Electrical Enginering The Technion Israel); Slobodan Vucetic
  
  
• 571: Inverse Optimal Control with Linearly Solvable MDPs
  Krishnamurthy Dvijotham (University of Washington); Emanuel Todorov (University of Washington)
  
  
• 576: Telling cause from effect based on high-dimensional observations
  Dominik Janzing (MPI for biological cybernetics); Patrik Hoyer; Bernhard Schoelkopf
  
  
• 582: Mining Clustering Dimensions
  Sajib Dasgupta (University of Texas at Dallas); Vincent Ng (University of Texas at Dallas)
  
  
• 586: Learning Tree Conditional Random Fields
  Joseph Bradley (Carnegie Mellon University); Carlos Guestrin (Cmu)
  
  
• 587: Learning efficiently with approximate inference via dual losses
  Ofer Meshi (HUJI); David Sontag (Mit); Tommi Jaakkola; Amir Globerson (Hebrew University)
  
  
• 588: Approximate Predictive Representations of Partially Observable Systems
  Doina Precup (Mcgill University); Monica Dinculescu (McGill University)
  
  
• 589: Bayes Optimal Multilabel Classification via Probabilistic Classifier Chains
  Krzysztof Dembczynski (Department Of Mathematics And Computer Science University Of Marburg Germany); Weiwei Cheng (University Marburg); Eyke Huellermeier (University of Marburg)
  
  
• 592: Non-Local Contrastive Objectives
  David Vickrey (Stanford University); Cliff Chiung-Yu Lin (Stanford University); Daphne Koller (Stanford University)
  
  
• 593: Constructing States for Reinforcement Learning
  M. M. Mahmud (Australian National University)
  
  
• 596: Graded Multilabel Classification: The Ordinal Case
  Weiwei Cheng (University Marburg); Krzysztof Dembczynski (Department Of Mathematics And Computer Science University Of Marburg Germany); Eyke Huellermeier (University of Marburg)
  
  
• 598: Finite-Sample Analysis of LSTD
  Alessandro Lazaric (Inria); Mohammad Ghavamzadeh (Inria); Remi Munos (Inria)
  
  
• 601: On the Interaction between Norm and Dimensionality: Multiple Regimes in Learning
  Percy Liang (University Of California - Berkeley); Nathan Srebro (TTI Chicago)
  
  
• 605: Learning Hierarchical Riffle Independent Groupings from Rankings
  Jonathan Huang (CMU); Carlos Guestrin (Cmu)
  
  
• 620: Active Learning for Multi-Task Adaptive Filtering
  Abhay Harpale (Carnegie Mellon University); Yiming Yang (Carnegie Mellon University)
  
  
• 627: Toward Off-Policy Learning Control with Function Approximation
  Hamid Maei (University of Alberta); Csaba Szepesvari (University Of Alberta); Shalabh Bhatnagar (Indian Institute of Science); Richard Sutton (University of Alberta)
  
  
• 628: Accelerated dual decomposition for MAP inference
  Vladimir Jojic (Stanford University); Stephen Gould (Stanford University); Daphne Koller (Stanford University)
  
  
• 636: Sparse Gaussian Process Regression via $\ell_1$ Penalization
  Feng Yan (Purdue University); Yuan Qi (Purdue University)
  
  
• 638: A theoretical analysis of feature pooling in vision algorithms
  Y-Lan Boureau (Nyu); Jean Ponce (Ens); Yann LeCun (New York University)
  
  
• 642: Comparing Clusterings in Space
  Michael Coen (UW-Madison); Hidayath Ansari (UW-Madison); Nathanael Fillmore (UW-Madison)
  
  
• 643: High-Performance Semi-Supervised Learning using Discriminatively Constrained Generative Models
  Gregory Druck (University of Massachusetts Am); Andrew McCallum (University of Massachusetts Amherst)
  
  
• 652: Nonparametric Return Distribution Approximation for Reinforcement Learning
  Tetsuro Morimura (IBM Research - Tokyo); Masashi Sugiyama (Tokyo Institute Of Technology); Hisashi Kashima (University of Tokyo); Hirotaka Hachiya; Toshiyuki Tanaka
  
  
• 654: Should one compute the Temporal Difference fix point or minimize the Bellman Residual? The unified oblique projection view
  Bruno Scherrer (Loria)