2007 ICML: Sessions

OSU Conference Services
100 LaSells Stewart Center
Corvallis, OR 97333
(541) 737-6439
(800) 678-6311

ICML 2007 Sessions Listings

Day 1: Thursday June 21

Two-view Feature Generation Model for Semi-supervised Learning
	[Abstract][Paper]
	Rie Ando - IBM T.J. Watson Research Center, USA Tong Zhang - Yahoo Inc., USA
The Rendezvous Algorithm: Multiclass Semi-Supervised Learning with Markov Random Walks
	[Abstract][Paper]
	Arik Azran - University of Cambridge, UK
Kernel Selection for Semi-Supervised Kernel Machines
	[Abstract][Paper]
	Guang Dai - Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong Dit-Yan Yeung - Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong
Neighbor Search with Global Geometry: A Minimax Message Passing Algorithm
	[Abstract][Paper]
	Kye-Hyeon Kim - Pohang University of Science and Technology, Korea Seungjin Choi - Pohang University of Science and Technology, Korea
Simple, Robust, Scalable Semi-supervised Learning via Expectation Regularization
	[Abstract][Paper]
	Gideon S. Mann - University of Massachusetts, USA Andrew McCallum - University of Massachusetts, USA

Learning Random Walks to Rank Nodes in Graphs
	[Abstract][Paper]
	Alekh Agarwal - IIT Bombay, India Soumen Chakrabarti - IIT Bombay, India
Focussed Crawling with Scalable Ordinal Regression Solvers
	[Abstract][Paper]
	Rashmin Babaria - Indian Institute of Science, INDIA Saketha Nath Jagarlapudi - Indian Institute of Science, INDIA Krishnan S. Kumar - Indian Institute of Science, INDIA Sivaramakrishnan Ramanujam Kaveri - Indian Institute of Science, INDIA Chiranjib Bhattacharyya - Indian Institute of Science, INDIA M. Narasimha Murty - Indian Institute of Science, INDIA
Learning to Rank: From Pairwise Approach to Listwise Approach
	[Abstract][Paper]
	Zhe Cao - Tsinghua University, China Tao Qin - Tsinghua University, China Tie-Yan Liu - Microsoft Research Asia, China Ming-Feng Tsai - National Taiwan University, China Hang Li - Microsoft Research Asia, China
Magnitude-Preserving Ranking Algorithms
	[Abstract][Paper]
	Corinna Cortes - Google Research, New York, USA Mehryar Mohri - Courant Institute of Mathematical Sciences and Google Research, USA Ashish Rastogi - Courant Institute of Mathematical Sciences, New York University, USA
On Learning Linear Ranking Functions for Beam Search
	[Abstract][Paper]
	Yuehua Xu - School of EECS, Oregon State University, USA Alan Fern - School of EECS, Oregon State University, USA

Learning Nonparametric Kernel Matrices from Pairwise Constraints
	[Abstract][Paper]
	Steven C. H. Hoi - The Chinese University of Hong Kong, China Rong Jin - Michigan State University, USA Michael R. Lyu - The Chinese University of Hong Kong, China
More Efficiency in Multiple Kernel Learning
	[Abstract][Paper]
	Alain Rakotomamonjy - Lab ITIS EA4051, Université de Rouen, France Francis R. Bach - CMM, Ecole des Mines de Paris, France Stephane Canu - Lab ITIS EA4051, INSA de Rouen, France Yves Grandvalet - IDIAP, Switzerland
A Kernel Path Algorithm for Support Vector Machines
	[Abstract][Paper]
	Gang Wang - Hong Kong University of Science and Technology, China Dit-Yan Yeung - Hong Kong University of Science and Technology, China Frederick Lochovsky - Hong Kong University of Science and Technology, China
Discriminant Kernel and Regularization Parameter Learning via Semidefinite Programming
	[Abstract][Paper]
	Jieping Ye - Department of Computer Science and Engineering, Arizona State University, USA Jianhui Chen - Department of Computer Science and Engineering, Arizona State University, USA Shuiwang Ji - Department of Computer Science and Engineering, Arizona State University, USA
Multiclass Multiple Kernel Learning
	[Abstract][Paper]
	Alexander Zien - MPI for Biological Cybernetics and Friedrich Miescher Laboratory, Tübingen, Germany Cheng Soon Ong - MPI for Biological Cybernetics and Friedrich Miescher Laboratory, Tübingen, Germany

Winnowing Subspaces
	[Abstract][Paper]
	Manfred K. Warmuth - University of California at Santa Cruz, USA
Sample Compression Bounds for Decision Trees
	[Abstract][Paper]
	Mohak Shah - CHUL Research Centre, Faculty of Medicine, Laval University, Canada
Online Discovery of Similarity Mappings
	[Abstract][Paper]
	Alexander Rakhlin - UC Berkeley, USA Jacob Abernethy - UC Berkeley, USA Peter L. Bartlett - UC Berkeley, USA
Approximate Maximum Margin Algorithms with Rules Controlled by the Number of Mistakes
	[Abstract][Paper]
	Petroula Tsampouka - University of Southampton, UK John Shawe-Taylor - University College London, UK
A Bound on the Label Complexity of Agnostic Active Learning
	[Abstract][Paper]
	Steve Hanneke - Carnegie Mellon University, USA

Bias/Variance Analysis for Relational Domains (ILP)
	[Abstract][Draft Paper]
	Jennifer Neville, Purdue University, USA David Jensen, University of Massachusetts Amherst, USA
Learning Probabilistic Logic Models from Probabilistic Examples (ILP)
	[Abstract][Draft Paper]
	Jianzhong Chen, Imperial College London, UK Stephen Muggleton, Imperial College London, UK Jose Santos, Imperial College London, UK
Learning from Interpretations: A Rooted Kernel for Ordered Hypergraphs
	[Abstract][Paper]
	Gabriel Wachman - Tufts University, USA Roni Khardon - Tufts University, USA
Statistical Predicate Invention
	[Abstract][Paper]
	Stanley Kok - University of Washington, USA Pedro Domingos - University of Washington, USA

Combining Online and Offline Knowledge in UCT
	[Abstract][Paper]
	Sylvain Gelly - Univ. Paris Sud, LRI, CNRS, INRIA, France David Silver - University of Alberta, Canada
Efficiently Computing Minimax Expected-Size Confidence Regions
	[Abstract][Paper]
	Brent Bryan - Machine Learning Department; Carnegie Mellon University, USA H. Brendan McMahan - Google Pittsburgh, USA Chad M. Schafer - Department of Statistics; Carnegie Mellon University, USA Jeff Schneider - Robotics Institute; Carnegie Mellon University, USA
Learning to Solve Game Trees
	[Abstract][Paper]
	David Stern - University of Cambridge, UK Ralf Herbrich - Microsoft Research Ltd., UK Thore Graepel - Microsoft Research Ltd., UK
On the Role of Tracking in Stationary Environments
	[Abstract][Paper]
	Richard S. Sutton - University of Alberta, Canada Anna Koop - University of Alberta, Canada David Silver - University of Alberta, Canada

Robust Multi-Task Learning with $t$-Processes
	[Abstract][Paper]
	Shipeng Yu - CAD and Knowledge Solutions, Siemens Medical Solutions, USA Volker Tresp - Corporate Technology, Siemens AG, Germany Kai Yu - NEC Lab America, USA
The Matrix Stick-Breaking Process for Flexible Multi-Task Learning
	[Abstract][Paper]
	Ya Xue - Department of Electrical & Computer Engineering, Duke University, USA David Dunson - Biostatistics Branch, National Institute of Environmental Health Sciences, USA Lawrence Carin - Department of Electrical & Computer Engineering, Duke University, USA
Self-taught Learning: Transfer Learning from Unlabeled Data
	[Abstract][Paper]
	Rajat Raina - Stanford University, USA Alexis Battle - Stanford University, USA Honglak Lee - Stanford University, USA Benjamin Packer - Stanford University, USA Andrew Y. Ng - Stanford University, USA
Learning a Meta-Level Prior for Feature Relevance from Multiple Related Tasks
	[Abstract][Paper]
	Su-In Lee - Stanford University, USA Vassil Chatalbashev - Stanford University, USA David Vickrey - Stanford University, USA Daphne Koller - Stanford University, USA

Intractability and Clustering with Constraints
	[Abstract][Paper]
	Ian Davidson - SUNY - Albany, USA S.S. Ravi - SUNY - Albany, USA
Cluster Analysis of Heterogeneous Rank Data
	[Abstract][Paper]
	Ludwig M. Busse - Institute of Computational Science, ETH Zurich, Switzerland Peter Orbanz - Institute of Computational Science, ETH Zurich, Switzerland Joachim M. Buhmann - Institute of Computational Science, ETH Zurich, Switzerland
Best of Both: A Hybridized Centroid-Medoid Clustering Heuristic
	[Abstract][Paper]
	Nizar Grira - National Institute of Informatics, Japan Michael E. Houle - National Institute of Informatics, Japan
Quantum Clustering Algorithms
	[Abstract][Paper]
	Esma Ämeur - Université de Montréal, Canada Gilles Brassard - Université de Montréal, Canada Sébastien Gambs - Université de Montréal, Canada

Discriminative Learning for Differing Training and Test Distributions
	[Abstract][Paper]
	Steffen Bickel - Max Planck Institute for Computer Science, Germany Michael Brüeckner - Max Planck Institute for Computer Science, Germany Tobias Scheffer - Max Planck Institute for Computer Science, Germany
Asymptotic Bayesian Generalization Error When Training and Test Distributions Are Different
	[Abstract][Paper]
	Keisuke Yamazaki - Tokyo Institute of Technology, Japan Motoaki Kawanabe - Fraunhofer FIRST IDA, Germany Sumio Watanabe - Tokyo Institute of Technology, Japan Masashi Sugiyama - Tokyo Institute of Technology, Japan Klaus-Robert Müller - Fraunhofer FIRST IDA, Technical University of Berlin, Germany
Experimental Perspectives on Learning from Imbalanced Data
	[Abstract][Paper]
	Jason D. Van Hulse - Florida Atlantic University, USA Taghi M. Khoshgoftaar - Florida Atlantic University, USA Amri Napolitano - Florida Atlantic University, USA
On the Value of Pairwise Constraints in Classification and Consistency
	[Abstract][Paper]
	Jian Zhang - Purdue University, USA Rong Yan - IBM Research, USA

Infinite Mixtures of Trees
	[Abstract][Paper]
	Sergey Kirshner - University of Alberta, Canada Padhraic Smyth - University of California, Irvine, USA
A Permutation-augmented Sampler for DP Mixture Models
	[Abstract][Paper]
	Percy Liang - UC Berkeley, USA Michael I. Jordan - UC Berkeley, USA Ben Taskar - University of Pennsylvania, USA
Multi-Task Learning for Sequential Data via iHMMs and the Nested Dirichlet Process
	[Abstract][Paper]
	Kai Ni - Duke University, USA Lawrence Carin - Duke University, USA David Dunson - Duke University, USA
Local Dependent Components
	[Abstract][Paper]
	Arto Klami - Helsinki University of Technology, Finland Samuel Kaski - Helsinki University of Technology, Finland

Learning Distance Function by Coding Similarity
	[Abstract][Paper]
	Aharon Bar Hillel - Intel research, Israel Daphna Weinshall - The Hebrew university of Jerusalem, Israel
Information-Theoretic Metric Learning
	[Abstract][Paper]
	Jason V. Davis - University of Texas at Austin, USA Brian Kulis - University of Texas at Austin, USA Prateek Jain - University of Texas at Austin, USA Suvrit Sra - University of Texas at Austin, USA Inderjit S. Dhillon - University of Texas at Austin, USA
A Transductive Framework of Distance Metric Learning by Spectral Dimensionality Reduction
	[Abstract][Paper]
	Fuxin Li - Institute of Automation, Chinese Academy of Sciences, China Jian Yang - Beijing University of Technology, China Jue Wang - Institute of Automation, Chinese Academy of Sciences, China
Dirichlet Aggregation: Unsupervised Learning towards an Optimal Metric for Proportional Data
	[Abstract][Paper]
	Hua-Yan Wang - State Key Laboratory of Machine Perception, Peking University, China Hongbin Zha - State Key Laboratory of Machine Perception, Peking University, China Hong Qin - Department of Computer Science, State University of New York at Stony Brook, USA

Parameter Learning for Relational Bayesian Networks
	[Abstract][Paper]
	Manfred Jaeger - Aalborg University, Denmark
Relational Clustering by Symmetric Convex Coding
	[Abstract][Paper]
	Bo Long - SUNY at Binghamton, USA Zhongfei Zhang - SUNY at Binghamton, USA Xiaoyun Wu - Google Inc., USA Philip S. Yu - IBM Watson Reasearch center, USA
Bottom-Up Learning of Markov Logic Network Structure
	[Abstract][Paper]
	Lilyana Mihalkova - The University of Texas at Austin, USA Raymond J. Mooney - The University of Texas at Austin, USA
Fast and Effective Kernels for Relational Learning from Texts
	[Abstract][Paper]
	Alessandro Moschitti - University of Trento, Italy Fabio Massimo Zanzotto - University of Rome, Italy

Day 2: Friday June 22

Bayesian Actor-Critic Algorithms
	[Abstract][Paper]
	Mohammad Ghavamzadeh - University of Alberta, Canada Yaakov Engel - University of Alberta, Canada
Automatic Shaping and Decomposition of Reward Functions
	[Abstract][Paper]
	Bhaskara Marthi - Massachusetts Institute of Technology, USA
Constructing Basis Functions from Directed Graphs for Value Function Approximation
	[Abstract][Paper]
	Jeffrey Johns - University of Massachusetts Amherst, U.S.A. Sridhar Mahadevan - University of Massachusetts Amherst, U.S.A.
Learning State-Action Basis Functions for Hierarchical MDPs
	[Abstract][Paper]
	Sarah Osentoski - University of Massachusetts Amherst, USA Sridhar Mahadevan - University of Massachusetts Amherst, USA
Analyzing Feature Generation for Value-Function Approximation
	[Abstract][Paper]
	Ronald Parr - Duke University, USA Christopher Painter-Wakefield - Duke University, USA Lihong Li - Rutgers University, USA Michael Littman - Rutgers University, USA

Most Likely Heteroscedastic Gaussian Process Regression
	[Abstract][Paper]
	Kristian Kersting - MIT CSAIL, USA Christian Plagemann - University of Freiburg, Germany Patrick Pfaff - University of Freiburg, Germany Wolfram Burgard - University of Freiburg, Germany
The Hierarchical Gaussian Process Latent Variable Model
	[Abstract][Paper]
	Neil D. Lawrence - School of Computer Science, University of Manchester, U.K. Andrew J. Moore - Department of Computer Science, University of Sheffield, U.K.
Discriminative Gaussian Process Latent Variable Models for Classification
	[Abstract][Paper]
	Raquel Urtasun - Computer Science and Artificial Intelligence Laboratory, MIT, USA Trevor Darrell - Computer Science and Artificial Intelligence Laboratory, MIT, USA
Multifactor Gaussian Process Models for Style-Content Separation
	[Abstract][Paper]
	Jack M. Wang - University of Toronto, Canada David J. Fleet - University of Toronto, Canada Aaron Hertzmann - University of Toronto, Canada
Nonmyopic Active Learning of Gaussian Processes: An Exploration--Exploitation Approach
	[Abstract][Paper]
	Andreas Krause - Carnegie Mellon University, USA Carlos Guestrin - Carnegie Mellon University, USA

Efficient Inference with Cardinality-based Clique Potentials
	[Abstract][Paper]
	Rahul Gupta - IBM Research Lab, New Delhi, India Ajit A. Diwan - IIT Bombay, India Sunita Sarawagi - IIT Bombay, India
What Is Decreased by the Max-sum Arc Consistency Algorithm?
	[Abstract][Paper]
	Tomáš Werner - Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Prague, Czech Republic
Robust Mixtures in the Presence of Measurement Errors
	[Abstract][Paper]
	Jianyong Sun - School of Computer Science and School of Physics and Astronomy, University of Birmingham, United Kingdom Ata Kaban - School of Computer Science, University of Birmingham, United Kingdom Somak Raychaudhury - School of Physics and Astronomy, University of Birmingham, United Kingdom
Dynamic Hierarchical Markov Random Fields and their Application to Web Data Extraction
	[Abstract][Paper]
	Jun Zhu - Tsinghua University, P. R. China Zaiqing Nie - Microsoft Research Asia, P. R. China Bo Zhang - Tsinghua University, P. R. China Ji-Rong Wen - Microsoft Research Asia, P. R. China
Restricted Boltzmann Machines for Collaborative Filtering
	[Abstract][Paper]
	Ruslan Salakhutdinov - University of Toronto, Canada Andriy Mnih - University of Toronto, Canada Geoffrey Hinton - University of Toronto, Canada

Scalable Training of L1-regularized Log-linear Models
	[Abstract][Paper]
	Galen Andrew - Microsoft Research, USA Jianfeng Gao - Microsoft Research, USA
Support Cluster Machine
	[Abstract][Paper]
	Bin Li - Department of Computer Science and Engineering, Fudan University, P. R. China Mingmin Chi - Department of Computer Science and Engineering, Fudan University, P. R. China Jianping Fan - Department of Computer Science, University of North Carolina, Charlotte., USA Xiangyang Xue - Department of Computer Science and Engineering, Fudan University, P. R. China
Trust Region Newton Methods for Large-Scale Logistic Regression
	[Abstract][Paper]
	Chih-Jen Lin - National Taiwan University, Taiwan Ruby Chiu-Hsing Weng - National Chengche University, Taiwan Sathiya Keerthi - Yahoo Research, USA
Large-scale RLSC Learning Without Agony
	[Abstract][Paper]
	Wenye Li - Dept. Computer Science and Engineering, the Chinese University of Hong Kong, China Kin-Hong Lee - Dept. Computer Science and Engineering, the Chinese University of Hong Kong, China Kwong-Sak Leung - Dept. Computer Science and Engineering, the Chinese University of Hong Kong, China
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
	[Abstract][Paper]
	Shai Shalev-Shwartz - Hebrew University, Israel Yoram Singer - Google Inc., USA Nathan Srebro - Toyota Technological Institute at Chicago, USA

A Novel Orthogonal NMF-Based Belief Compression for POMDPs
	[Abstract][Paper]
	Xin Li - Computer Science Department, Hong Kong Baptist University, Hong Kong, China William K. W. Cheung - Computer Science Department, Hong Kong Baptist University, Hong Kong, China Jiming Liu - Computer Science Department, Hong Kong Baptist University, Hong Kong, China Zhili Wu - Computer Science Department, Hong Kong Baptist University, Hong Kong, China
Percentile Optimization in Uncertain MDP with Application to Efficient Exploration
	[Abstract][Paper]
	Erick Delage - Stanford University, USA Shie Mannor - McGill University, Canada
Multi-armed Bandit Problems with Dependent Arms
	[Abstract][Paper]
	Sandeep Pandey - Yahoo, USA Deepayan Chakrabarti - Yahoo, USA Deepak Agarwal - Yahoo, USA
Reinforcement Learning by Reward-weighted Regression for Operational Space Control
	[Abstract][Paper]
	Jan Peters - Max Planck Institute for Biological Cybernetics, Germany Stefan Schaal - University of Southern California, USA

Multiple Instance Learning for Sparse Positive Bags
	[Abstract][Paper]
	Razvan C. Bunescu - University of Texas at Austin, USA Raymond J. Mooney - University of Texas at Austin, USA
On the Relation Between Multi-Instance Learning and Semi-Supervised Learning
	[Abstract][Paper]
	Zhi-Hua Zhou - Nanjing University, China Jun-Ming Xu - Nanjing University, China
CarpeDiem: an Algorithm for the Fast Evaluation of SSL Classifiers
	[Abstract][Paper]
	Roberto Esposito - Università di Torino, Italy Daniele P. Radicioni - Università di Torino, Italy
Modeling Changing Dependency Structure in Multivariate Time Series
	[Abstract][Paper]
	Xiang Xuan - UBC, Canada Kevin Murphy - UBC, Canada

Recovering Temporally Rewiring Networks: A model-based approach
	[Abstract][Paper]
	Fan Guo - Carnegie Mellon University, USA Steve Hanneke - Carnegie Mellon University, USA Wenjie Fu - Carnegie Mellon University, USA Eric P. Xing - Carnegie Mellon University, USA
Scalable Modeling of Real Graphs using Kronecker Multiplication
	[Abstract][Paper]
	Jure Leskovec - Carnegie Mellon University, USA Christos Faloutsos - Carnegie Mellon University, USA
Graph Clustering With Network Structure Indices
	[Abstract][Paper]
	Matthew J. Rattigan - University of Massachusetts Amherst, USA Marc Maier - University of Massachusetts Amherst, USA David Jensen - University of Massachusetts Amherst, USA
Entire Regularization Paths for Graph Data
	[Abstract][Paper]
	Koji Tsuda - Max Planck Institute for Biological Cybernetics, Germany

Uncovering Shared Structures in Multiclass Classification
	[Abstract][Paper]
	Yonatan Amit - Hebrew University, Jerusalem, Isarel Michael Fink - Hebrew University, Jerusalem, Isarel Nathan Srebro - Toyota Technological Institute at Chicago, U.S. Shimon Ullman - Weizmann Institute of Science, Israel
Multiclass Core Vector Machine
	[Abstract][Paper]
	S. Asharaf - Computer Science and Automation, Indian Institute of Science, Bangalore - 560012, India M. Narasimha Murty - Computer Science and Automation, Indian Institute of Science, Bangalore - 560012, India S. K. Shevade - Computer Science and Automation, Indian Institute of Science, Bangalore - 560012, India
Simpler Core Vector Machines with Enclosing Balls
	[Abstract][Paper]
	Ivor W. Tsang - Hong Kong University of Science and Technology, Hong Kong Andras Kocsor - University of Szeged, Hungary James T. Kwok - Hong Kong University of Science and Technology, Hong Kong
Solving MultiClass Support Vector Machines with LaRank
	[Abstract][Paper]
	Antoine Bordes - LIP, Universite de Paris 6,104 Avenue du Pdt Kennedy, 75016 Paris, France Léon Bottou - NEC Laboratories America, Inc.,, USA Patrick Gallinari - LIP, Universite de Paris 6,104 Avenue du Pdt Kennedy, 75016 Paris, France Jason Weston - NEC Laboratories America, Inc.,, USA

Learning to Compress Images and Video
	[Abstract][Paper]
	Li Cheng - National ICT Australia, Australia S.V.N. Vishwanathan - National ICT Australia, Australia
Linear and Nonlinear Generative Probabilistic Class Models for Shape Contours
	[Abstract][Paper]
	Graham McNeill - University of Edinburgh, UK Sethu Vijayakumar - University of Edinburgh, UK
Adaptive Mesh Compression in 3D Computer Graphics using Multiscale Manifold Learning
	[Abstract][Paper]
	Sridhar Mahadevan - University of Massachusetts, Amherst, USA
Map Building without Localization by Dimensionality Reduction Techniques
	[Abstract][Paper]
	Takehisa Yairi - University of Tokyo, Japan

Discriminant Analysis in Correlation Similarity Measure Space
	[Abstract][Paper]
	Yong Ma - Sensing & Control Lab., Omron Corporation, Japan Shihong Lao - Sensing & Control Lab., Omron Corporation, Japan Erina Takikawa - Sensing & Control Lab., Omron Corporation, Japan Masato Kawade - Sensing & Control Lab., Omron Corporation, Japan
Local Similarity Discriminant Analysis
	[Abstract][Paper]
	Luca Cazzanti - Applied Physics Lab-University of Washington, USA Maya Gupta - University of Washington, USA
Least Squares Linear Discriminant Analysis
	[Abstract][Paper]
	Jieping Ye - Department of Computer Science and Engineering, Arizona State University, USA
A Fast Linear Separability Test by Projection of Positive Points on Subspaces
	[Abstract][Paper]
	Yogananda A. P. - Satyam Computer Services Ltd., India M. Narasimha Murty - Indian Institute of Science, India Lakshmi Gopal - Satyam Computer Services Ltd., India

Feature Selection in Kernel Space
	[Abstract][Paper]
	Bin Cao - Peking University, China Dou Shen - Hong Kong University of Science and Technology, China Jian-Tao Sun - Microsoft Research Asia, China Qiang Yang - Hong Kong University of Science and Technology, China Zheng Chen - Microsoft Research Asia, China
Minimum Reference Set Based Feature Selection for Small Sample Classifications
	[Abstract][Paper]
	Xue-wen Chen - Department of Electrical Engineering and Computer Science, The University of Kansas, USA Jong Cheol Jeong - Department of Electrical Engineering and Computer Science, The University of Kansas, USA
Supervised Feature Selection via Dependence Estimation
	[Abstract][Paper]
	Le Song - National ICT Australia, Australia Alex Smola - National ICT Australia, Australia Arthur Gretton - MPI Tübingen, Germany Karsten M. Borgwardt - LMU München, Germany Justin Bedo - National ICT Australia, Australia
Spectral Feature Selection for Supervised and Unsupervised Learning
	[Abstract][Paper]
	Zheng Zhao - Arizona State University, USA Huan Liu - Arizona State University, USA

Dimensionality Reduction and Generalization
	[Abstract][Paper]
	Sofia Mosci - DIFI & DISI, Universita' di Genova, Italy Lorenzo Rosasco - DISI, Universita' di Genova, Italy Alessandro Verri - DISI, Universita' di Genova, Italy
Regression on Manifolds using Kernel Dimension Reduction
	[Abstract][Paper]
	Jens Nilsson - Centre for Mathematical Sciences, Lund University, Sweden Fei Sha - Computer Science Division, University of California, Berkeley, USA Michael I. Jordan - Computer Science Division and Department of Statistics, University of California, Berkeley, USA
Transductive Regression Piloted by Inter-Manifold Relations
	[Abstract][Paper]
	Huan Wang - The Chinese University of Hong Kong, China Shuicheng Yan - University of Illinois at Urbana-Champaign, USA Thomas Huang - University of Illinois at Urbana-Champaign, USA Jianzhuang Liu - The Chinese University of Hong Kong, China Xiaoou Tang - The Chinese University of Hong Kong, China
Local Learning Projections
	[Abstract][Paper]
	Mingrui Wu - Max Planck Institute for Biological Cybernetics, Germany Kai Yu - NEC Labs America, USA Shipeng Yu - Siemens Medical Solutions, USA Bernhard Schölkopf - Max Planck Institute for Biological Cybernetics, Germany

Day 3: Saturday June 23

Sparse Probabilistic Classifiers
	[Abstract][Paper]
	Romain Hérault - Heudiasyc UMR 6599 Universite de Technologie de Compiegne, France Yves Grandvalet - Heudiasyc UMR 6599 Universite de Technologie de Compiegne, France
Direct Convex Relaxations of Sparse SVM
	[Abstract][Paper]
	Antoni B. Chan - University of California, San Diego, USA Nuno Vasconcelos - University of California, San Diego, USA Gert R. G. Lanckriet - University of California, San Diego, USA
A Recursive Method for Discriminative Mixture Learning
	[Abstract][Paper]
	Minyoung Kim - Rutgers University, USA Vladimir Pavlovic - Rutgers University, USA
Quadratically Gated Mixture of Experts for Incomplete Data Classification
	[Abstract][Paper]
	Xuejun Liao - Duke University, USA Hui Li - Duke University, USA Lawrence Carin - Duke University, USA
Classifying Matrices with a Spectral Regularization
	[Abstract][Paper]
	Ryota Tomioka - Dept. of Mathematical Informatics, IST, The University of Tokyo, Japan Kazuyuki Aihara - Institute of Industrial Science, The University of Tokyo, Japan

Exponentiated Gradient Algorithms for Log-Linear Structured Prediction
	[Abstract][Paper]
	Amir Globerson - MIT, USA Terry Koo - MIT, USA Xavier Carreras - MIT, USA Michael Collins - MIT, USA
Comparisons of Sequence Labeling Algorithms and Extensions
	[Abstract][Paper]
	Nam Nguyen - Department of Computer Science, Cornell University, USA Yunsong Guo - Department of Computer Science, Cornell University, USA
Piecewise Pseudolikelihood for Efficient Training of Conditional Random Fields
	[Abstract][Paper]
	Charles Sutton - University of Massachusetts, United States Andrew McCallum - University of Massachusetts, United States
Incremental Bayesian Networks for Structure Prediction
	[Abstract][Paper]
	Ivan Titov - University of Geneva, Switzerland James Henderson - University of Edinburgh, United Kingdom
Transductive Support Vector Machines for Structured Variables
	[Abstract][Paper]
	Alexander Zien - Max Planck Institute for Biological Cybernetics and Friedrich Miescher Laboratory, Germany Ulf Brefeld - Max Planck Institute for Computer Science, Germany Tobias Scheffer - Max Planck Institute for Computer Science, Germany

A Dependence Maximization View of Clustering
	[Abstract][Paper]
	Le Song - National ICT Australia, Australia Alex Smola - National ICT Australia, Australia Arthur Gretton - MPI Tübingen, Germany Karsten M. Borgwardt - LMU München, Germany
Revisiting Probabilistic Models for Clustering with Constraints
	[Abstract][Paper]
	Blaine Nelson - University of California, Berkeley, USA Ira Cohen - Hewlett-Packard Research Labs, USA
Supervised Clustering of Streaming Data for Email Batch Detection
	[Abstract][Paper]
	Peter Haider - Max Planck Institute for Computer Science, Germany Ulf Brefeld - Max Planck Institute for Computer Science, Germany Tobias Scheffer - Max Planck Institute for Computer Science, Germany
Maximum Margin Clustering Made Practical
	[Abstract][Paper]
	Kai Zhang - Hong Kong University of Science and Technology, Hong Kong Ivor W. Tsang - Hong Kong University of Science and Technology, Hong Kong James T. Kwok - Hong Kong University of Science and Technology, Hong Kong
Spectral Clustering with Multiple Views
	[Abstract][Paper]
	Dengyong Zhou - Microsoft Research, USA Christopher J.C. Burges - Microsoft Research, USA

Unsupervised Prediction of Citation Influences
	[Abstract][Paper]
	Laura Dietz - Max Planck Institute for Computer Science, Germany Steffen Bickel - Max Planck Institute for Computer Science, Germany Tobias Scheffer - Max Planck Institute for Computer Science, Germany
Three New Graphical Models for Statistical Language Modelling
	[Abstract][Paper]
	Andriy Mnih - University of Toronto, Canada Geoffrey Hinton - University of Toronto, Canada
Mixtures of Hierarchical Topics with Pachinko Allocation
	[Abstract][Paper]
	David Mimno - University of Massachusetts, Amherst, USA Wei Li - University of Massachusetts, Amherst, USA Andrew McCallum - University of Massachusetts, Amherst, USA
Unsupervised Estimation for Noisy-Channel Models
	[Abstract][Paper]
	Markos Mylonakis - University of Amsterdam, The Netherlands Khalil Sima'an - University of Amsterdam, The Netherlands Rebecca Hwa - University of Pittsburgh, USA
Hierarchical Maximum Entropy Density Estimation
	[Abstract][Paper]
	Miroslav Dudík - Princeton University, U.S. David M. Blei - Princeton University, U.S. Robert E. Schapire - Princeton University, U.S.

Learning to Combine Distances for Complex Representations
	[Abstract][Paper]
	Adam Woznica - University of Geneva, Switzerland Alexandros Kalousis - University of Geneva, Switzerland Melanie Hilario - University of Geneva, Switzerland
Optimal Dimensionality of Metric Space for Classification
	[Abstract][Paper]
	Wei Zhang - Department of Computer Science and Engineering, Fudan University, China Xiangyang Xue - Department of Computer Science and Engineering, Fudan University, China Zichen Sun - Department of Computer Science and Engineering, Fudan University, China Yue-Fei Guo - Department of Computer Science and Engineering, Fudan University, China Hong Lu - Department of Computer Science and Engineering, Fudan University, China
Learning for Efficient Retrieval of Structured Data with Noisy Queries
	[Abstract][Paper]
	Charles Parker - Oregon State University, United States Alan Fern - Oregon State University, United States Prasad Tadepalli - Oregon State University, United States

Structural Alignment based Kernels for Protein Structure Classification
	[Abstract][Paper]
	Sourangshu Bhattacharya - Indian Institute of Science, India Chiranjib Bhattacharyya - Indian Institute of Science, India Nagasuma R. Chandra - Indian Institute of Science, India
An Integrated Approach to Feature Invention and Model Construction for Drug Activity Prediction
	[Abstract][Paper]
	Jesse Davis - University of Wisconsin-Madison, USA Vítor Santos Costa - University of Porto, Portugal Soumya Ray - Oregon State University, USA David Page - University of Wisconsin-Madison, USA
Hybrid Huberized Support Vector Machines for Microarray Classification
	[Abstract][Paper]
	Li Wang - Ross School of Business, University of Michigan, U.S. Ji Zhu - Department of Statistics, University of Michigan, U.S. Hui Zou - School of Statistics, University of Minnesota, U.S.

A Kernel-based Causal Learning Algorithm
	[Abstract][Paper]
	Xiaohai Sun - Max Planck Institute for Biological Cybernetics, Germany Dominik Janzing - Universitaet Karlsruhe (TH), Germany Bernhard Schölkopf - Max Planck Institute for Biological Cybernetics, Germany Kenji Fukumizu - Institute of Statistical Mathematics, 4-6-7 Minami-Azabu, Minato-ku, Tokyo 106-8569, Japan
Kernelizing PLS, Degrees of Freedom, and Efficient Model Selection
	[Abstract][Paper]
	Nicole Krämer - TU Berlin, Germany Mikio L. Braun - TU Berlin, Germany
An Empirical Evaluation of Deep Architectures on Problems with Many Factors of Variation
	[Abstract][Paper]
	Hugo Larochelle - Université de Montréal, Canada Dumitru Erhan - Université de Montréal, Canada Aaron Courville - Université de Montréal, Canada James Bergstra - Université de Montréal, Canada Yoshua Bengio - Université de Montréal, Canada

Beamforming using the Relevance Vector Machine
	[Abstract][Paper]
	David Wipf - University of California, San Francisco, USA Srikantan Nagarajan - University of California, San Francisco, USA
Nonlinear Independent Component Analysis with Minimal Nonlinear Distortion
	[Abstract][Paper]
	Kun Zhang - The Chinese University of Hong Kong, Hong Kong Laiwan Chan - The Chinese University of Hong Kong, Hong Kong
On One Method of Non-Diagonal Regularization in Sparse Bayesian Learning
	[Abstract][Paper]
	Dmitry Kropotov - Dorodnicyn Computing Centre of the Russian Academy of Sciences, Russia Dmitry Vetrov - Dorodnicyn Computing Centre of the Russian Academy of Sciences, Russia

Non-Isometric Manifold Learning: Analysis and an Algorithm
	[Abstract][Paper]
	Piotr Dollár - University Of California, San Diego, United States Vincent Rabaud - University Of California, San Diego, United States Serge Belongie - University Of California, San Diego, United States
Manifold-adaptive dimension estimation
	[Abstract][Paper]
	Amir massoud Farahmand - University of Alberta, Canada Csaba Szepesvári - University of Alberta, Canada Jean-Yves Audibert - Certis - Ecole des Ponts - ParisTech, France
Robust Non-linear Dimensionality Reduction using Successive 1-Dimensional Laplacian Eigenmaps
	[Abstract][Paper]
	Samuel Gerber - University of Utah, US Tolga Tasdizen - University of Utah, US Ross Whitaker - University of Utah, US
Adaptive Dimension Reduction Using Discriminant Analysis and $K$-means Clustering
	[Abstract][Paper]
	Tao Li - Florida International University, USA Chris Ding - Lawrence Berkeley National Lab, USA

Bayesian Compressive Sensing and Projection Optimization
	[Abstract][Paper]
	Shihao Ji - Duke University, USA Lawrence Carin - Duke University, USA
Online Kernel PCA with Entropic Matrix Updates
	[Abstract][Paper]
	Dima Kuzmin - University of California - Santa Cruz, USA Manfred K. Warmuth - University of California - Santa Cruz, USA
Sparse Eigen Methods by D.C. Programming
	[Abstract][Paper]
	Bharath Sriperumbudur - University of California, San Diego, USA David Torres - University of California, San Diego, USA Gert Lanckriet - University of California, San Diego, USA
Full Regularization Path for Sparse Principal Component Analysis
	[Abstract][Paper]
	Alexandre d'Aspremont - Princeton University, USA Francis R. Bach - Ecole des Mines de Paris, France Laurent El Ghaoui - U.C. Berkeley, USA

Boosting for Transfer Learning
	[Abstract][Paper]
	Wenyuan Dai - Shanghai Jiao Tong University, China Qiang Yang - Hong Kong University of Science and Technology, Hong Kong, China Gui-Rong Xue - Shanghai Jiao Tong University, China Yong Yu - Shanghai Jiao Tong University, China
Gradient Boosting for Kernelized Output Spaces
	[Abstract][Paper]
	Pierre Geurts - University of Liège, Belgium Louis Wehenkel - University of Liège, Belgium Florence d'Alché-Buc - University of Evry, France
Asymmetric Boosting
	[Abstract][Paper]
	Hamed Masnadi-Shirazi - UCSD, USA Nuno Vasconcelos - UCSD, USA
On Learning with Dissimilarity Functions
	[Abstract][Paper]
	Liwei Wang - Peking University, China Cheng Yang - Peking University, China Jufu Feng - Peking University, China

Tracking Value Function Dynamics to Improve Reinforcement Learning with Piecewise Linear Function Approximation
	[Abstract][Paper]
	Chee Wee Phua - National ICT Australia, Australia Robert Fitch - National ICT Australia, Australia
Cross-Domain Transfer for Reinforcement Learning
	[Abstract][Paper]
	Matthew E. Taylor - The University of Texas at Austin, USA Peter Stone - The University of Texas at Austin, USA
Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach
	[Abstract][Paper]
	Aaron Wilson - Oregon State University, USA Alan Fern - Oregon State University, USA Prasad Tadepalli - Oregon State University, USA Soumya Ray - Oregon State University, USA
Conditional Random Fields for Multi-agent Reinforcement Learning
	[Abstract][Paper]
	Xinhua Zhang - CSL, RSISE, Australian National University, and SML NICTA, Australia Douglas Aberdeen - NICTA, Australian National University, Australia S.V.N. Vishwanathan - SML NICTA, and CSL, RSISE, Australian National University, Australia