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ICML 2007 Sessions Listings

Day 1: Thursday June 21
Session 1: Semi-Supervised Learning (Ag Production)
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
Session 2: Ranking (Ag Leaders)
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
Session 3: Kernel Methods (C&E Hall)
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
Session 4: Online Learning and Theory (Ag Science)
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
Session 5: Relational Learning and ILR - Joint Session (Austin Auditorium)
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
Session 6: COMPUTER GO, RL AND GAME THEORY (Ag Leaders)
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
Session 7: MULTI-TASK AND TRANSFER LEARNING (C&E Hall)
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
Session 8: CLUSTERING I (Ag Production)
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
Session 9: CLASSIFICATION I (Ag Leaders)
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
Session 10: NONPARAMETRIC BAYESIAN METHODS (C&E Hall)
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
Session 11: METRIC LEARNING I (Ag Production)
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
Session 12: RELATIONAL LEARNING II (Austin Auditorium)
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
Session 13: REINFORCEMENT LEARNING I (Austin Auditorium)
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
Session 14: GAUSSIAN PROCESSES (Ag Leaders)
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
Session 15: INFERENCE, PROBABILISTIC MODELS, AND RANDOM FIELDS (C&E Hall)
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
Session 16: LARGE-SCALE OPTIMIZATION (Ag Production)
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
Session 17: REINFORCEMENT LEARNING II (Austin Auditorium)
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
Session 18: MULTIPLE-INSTANCE AND SEQUENTIAL LEARNING (Ag Production)
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
Session 19: NETWORKS AND GRAPHS (C&E Hall)
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
Session 20: CLASSIFICATION II (Ag Leaders)
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
Session 21: VISION, GRAPHICS AND ROBOTICS (C&E Hall)
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
Session 22: DISCRIMINANT ANALYSIS (Ag Leaders)
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
Session 23: FEATURE SELECTION (Ag Production)
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
Session 24: MANIFOLDS AND DIMENSIONALITY REDUCTION I (Austin Auditorium)
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
Session 25: CLASSIFICATION III (Ag Leaders)
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
Session 26: STRUCTURED PREDICTION (Austin Auditorium)
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
Session 27: CLUSTERING II (Ag Production)
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
Session 28: LANGUAGE, TOPIC MODELLING AND HIERARCHIES (C&E Hall)
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.
Session 29: METRIC LEARNING II (C&E Hall)
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
Session 30: BIOINFORMATICS (Ag Production)
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.
Session 31: CAUSALITY, KERNELS AND DEEP NETWORKS (Austin Auditorium)
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
Session 32: SPARSE MODELS AND SIGNAL PROCESSING (Ag Leaders)
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
Session 33: MANIFOLDS AND DIMENSIONALITY REDUCTION II (C&E Hall)
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
Session 34: SPARSE METHODS AND PCA (Ag Production)
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
Session 35: BOOSTING (Ag Leaders)
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
Session 36: REINFORCEMENT LEARNING III (Austin Auditorium)
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