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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
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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)