Session Schedule

_{Robert Schapire, Princeton University: Maximum Entropy and Species Distribution Modeling}

_{Note: Plenary talks will be held in the McConomy Auditorium}

_{SVMs and Kernel Methods 1
Chair: Alex Smola}

• _{(228) The Support Vector Decomposition Machine by Francisco Pereira, Geoff Gordon}
• _{(483) Learning Low-Rank Kernel Matrices by Brian Kulis, Matyas Sustik, Inderjit Dhillon}
• _{(383) A Statistical Approach to Rule Learning by Ulrich Rückert, Stefan Kramer}
• _{(529) Multiclass Reduced-Set Support Vector Machines by Benyang Tang, Dominic Mazzoni}

_{Boosting
Chair: Rob Schapire}

• _{(418) Totally Corrective Boosting Algorithms that Maximize the Margin by Manfred Warmuth, Jun Liao, Gunnar Raetsch}
• _{(484) Multiclass Boosting with Repartitioning by Ling Li}
• _{(277) Learning a Kernel Function for Classification with Small Training Samples by Tomer Hertz, Aharon Bar Hillel, Daphna Weinshall}
• _{(600) How Boosting the Margin Can Also Boost Classifier Complexity by Lev Reyzin, Robert Schapire}

_{Modeling Text
Chair: Dunja Mladenic}

• _{(365) A Probabilistic Model for Text Kernels by Alain Lehmann, John Shawe-Taylor}
• _{(49) Clustering Documents with an Exponential-Family Approximation of the Dirichlet Compound Multinomial Distribution by Charles Elkan}
• _{(70) The Rate Adapting Poisson Model for Information Retrieval and Object Recognition by Peter Gehler, Alex Holub, Max Welling}
• _{(371) Topic Modeling: Beyond Bag-of-Words by Hanna Wallach}

_{Regression
Chair: Chris Williams}

• _{(11) Regression with the Optimised Combination Technique by Jochen Garcke}
• _{(117) Efficient Co-Regularized Least Squares Regression by Ulf Brefeld, Thomas Gaertner, Tobias Scheffer, Stefan Wrobel}
• _{(476) Bayesian Regression with Input Noise for High Dimensional Data by Jo-Anne Ting, Aaron D'Souza, Stefan Schaal}
• _{(137) Two-Dimensional Solution Path for Support Vector Regression by Gang Wang, Dit-Yan Yeung, Frederick H. Lochovsky}

_{SVMs and Kernel Methods 2
Chair: Michael Jordan}

• _{(610) Deterministic Annealing for Semi-supervised Kernel Machines by Vikas Sindhwani, Sathiya Keerthi, Olivier Chapelle}
• _{(448) A DC Algorithm for Kernel Selection by Andreas Argyriou, Raphael Hauser, Charles A. Micchelli, Massimiliano Pontil}
• _{(454) On a Theory of Kernels as Similarity Functions by Maria-Florina Balcan, Avrim Blum}
• _{(237) Pareto Optimal Linear Classification by Seung-Jean Kim, Alessandro Magnani, Sikandar Samar, Stephen Boyd, Johan Lim}

_{Learning Theory
Chair: Sally Goldman}

• _{(392) On Bayesian Bounds by Arindam Banerjee}
• _{(394) CN=CPCN by Liva Ralaivola, Francois Denis, Christophe Magnan}
• _{(332) Efficient Learning of Naive Bayes Classifiers under Class-Conditional Classification Noise by Francois Denis, Christophe Nicolas Magnan, Liva Ralaivola}
• _{(279) Learning to Impersonate by Moni Naor, Guy Rothblum}

_{Methodology and Experimental Studies
Chair: Artur Dubrawski}

• _{(533) Algorithms for Portfolio Management based on the Newton Method by Amit Agarwal, Elad Hazan, Satyen Kale, Robert Schapire}
• _{(128) The Relationship Between Precision-Recall and ROC Curves by Jesse Davis, Mark Goadrich}
• _{(66) Feature Subset Selection Bias for Classification Learning by Surendra Singhi, Huan Liu}
• _{(607) An Empirical Comparison of Supervised Learning Methods by Rich Caruana, Alexandru Niculescu-Mizil}

_{Multiple Tasks
Chair: Marie desJardins}

• _{(679) Categorisation in multiple category systems by Jean-Michel Renders, Eric Gaussier, Cyril Goutte, Francois Pacull, Gabriela Csurka}
• _{(430) Collaborative Ordinal Regression by Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Kriegel}
• _{(59) Estimating Relatedness via Data Compression by Brendan Juba}
• _{(678) Online Multiclass Learning by Interclass Hypothesis Sharing by Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman}

_{RL 1: Exploration
Chair: Doina Precup}

• _{(627) Experience-Efficient Learning in Associative Bandit Problems by Alexander Strehl, Christopher Mesterharm, Michael Littman, Haym Hirsh}
• _{(435) An Intrinsic Reward Mechanism for Efficient Exploration by Ozgur Simsek, Andrew Barto}
• _{(675) Qualitative Reinforcement Learning by Arkady Epshteyn, Gerald DeJong}

_{Ensembles
Chair: Paul Bennett}

• _{(632) Using Query-Specific Variance Estimates to Combine Bayesian Classifiers by Chi-Hoon Lee, Russell Greiner, Shaojun Wang}
• _{(76) Pruning in Ordered Bagging Ensembles by Gonzalo Martí­nez-Muñoz, Alberto Suárez}
• _{(507) Collaborative Prediction Using Ensembles of Maximum Margin Matrix Factorizations by Dennis DeCoste}

_{Bioinformatics
Chair: Steven Scott}

• _{(215) Convex Optimization Techniques for Fitting Sparse Gaussian Graphical Models by Onureena Banerjee, Laurent El Ghaoui, Alexandre d'Aspremont, Georges Natsoulis}
• _{(465) A graphical model for predicting protein molecular function by Barbara Engelhardt, Michael Jordan, Steven Brenner}
• _{(494) Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture by Eric Xing, Kyung-Ah Sohn, Michael Jordan, Yee Whye Teh}

_{Clustering 1: Spectral Methods
Chair: Nathan Srebro}

• _{(212) A Duality View of Spectral Methods for Dimensionality Reduction by Lin Xiao, Jun Sun, Stephen Boyd}
• _{(147) The uniqueness of a good optimum for K-means by Marina Meila}
• _{(510) Generalized Spectral Bounds for Sparse LDA by Baback Moghaddam, Yair Weiss, Shai Avidan}

_{Mandyam V. Srinivasan, Australian National University: Small Brains, Smart Minds: Vision, Navigation, and 'Cognition' in Honeybees}

_{Note: Plenary talks will be held in the McConomy Auditorium}

_{RL 2: New Algorithms
Chair: Drew Bagnell}

• _{(582) PAC Model-free Reinforcement Learning by Alexander Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael Littman}
• _{(197) An Analytic Solution to Discrete Bayesian Reinforcement Learning by Pascal Poupart, Nikos Vlassis, Jesse Hoey, Kevin Regan}
• _{(670) Using Inaccurate Models in Reinforcement Learning by Pieter Abbeel, Morgan Quigley, Andrew Ng}
• _{(199) Fast Direct Policy Evaluation using Multiscale Analysis of Markov Diffusion Processes by Mauro Maggioni, Sridhar Mahadevan}

_{SVMs and Kernel Methods 3
Chair: Patrick Haffner}

• _{(238) Optimal Kernel Selection in Kernel Fisher Discriminant Analysis by Seung-Jean Kim, Alessandro Magnani, Stephen Boyd}
• _{(136) Hierarchical Classification: Combining Bayes with SVM by Nicolò Cesa-Bianchi, Claudio Gentile, Luca Zaniboni}
• _{(223) Permutation Invariant SVMs by Pannagadatta Shivaswamy, Tony Jebara}
• _{(170) Simpler Knowledge-based Support Vector Machines by Quoc V. Le, Alex Smola, Thomas Gaertner}

_{Active Learning
Chair: Stefan Wrobel}

• _{(80) Active Sampling for Detecting Irrelevant Features by Sriharsha Veeramachaneni, Emanuele Olivetti, Paolo Avesani}
• _{(397) Active Learning via Transductive Experimental Design by Kai Yu, Jinbo Bi, Volker Tresp}
• _{(144) Batch Mode Active Learning and Its Application to Medical Image Classification by Steven C. H. Hoi, Rong Jin, Jianke Zhu, Michael R. Lyu}
• _{(551) Agnostic Active Learning by Maria-Florina Balcan, Alina Beygelzimer, John Langford}

_{Clustering 2: PCA and Other Methods
Chair: Bikash Sabata}

• _{(713) Robust Probabilistic Projections by Cedric Archambeau, Nicolas Delannay, Michel Verleysen}
• _{(697) R1-PCA: Rotational Invariant L1-norm Principal Component Analysis for Robust Subspace Factorization by Chris Ding, Ding Zhou, Xiaofeng He, Hongyuan Zha}
• _{(684) Local Distance Preservation in the GP-LVM through Back Constraints by Neil D. Lawrence, Joaquin Quiñonero-Candela}
• _{(236) Fast Nonparametric Clustering with Gaussian Blurring Mean-Shift by Miguel Carreira-Perpinan}

_{RL 3: PSRs,Shaping,and Nonstationarity
Chair: Sridhar Mahadevan}

• _{(124) Autonomous Shaping: Knowledge Transfer in Reinforcement Learning by George Konidaris, Andrew Barto}
• _{(407) Dealing with Non-Stationary Environments using Context Detection by Bruno Castro da Silva, Eduardo Wisnieski Basso, Ana Lucia Cetertich Bazzan, Paulo Martins Engel}
• _{(665) Learning Predictive State Representations Using Non-Blind Policies by Michael Bowling, Peter McCracken, Michael James, James Neufeld, Dana Wilkinson}
• _{(370) Predictive State Representations with Options by Britton Wolfe, Satinder Singh}

_{Structured Outputs 1
Chair: Tobias Scheffer}

• _{(500) Online Decoding of Markov Models with Latency Constraints by Mukund Narasimhan, Paul Viola, Michael Shilman}
• _{(306) Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks by Alex Graves, Santiago Fernandez, Faustino Gomez, Juergen Schmidhuber}
• _{(704) Efficient inference on sequence segmentation models by Sunita Sarawagi}
• _{(442) Learning User Preferences for Sets of Objects by Marie desJardins, Eric Eaton, Kiri Wagstaff}

_{Applications
Chair: David "Pablo" Cohn}

• _{(398) Bayesian Pattern Ranking for Move Prediction in the Game of Go by David Stern, Ralf Herbrich, Thore Graepel}
• _{(480) Personalized Handwriting Recognition via Biased Regularization by Wolf Kienzle, Kumar Chellapilla}
• _{(671) Statistical Debugging: Simultaneous Identification of Multiple Bugs by Alice Zheng, Michael Jordan, Ben Liblit, Mayur Naik, Alex Aiken}
• _{(234) Classifying EEG for Brain-Computer Interfaces: Learning Optimal Filters for Dynamical System Features by Le Song, Julien Epps}

_{Clustering 3
Chair: Sam Roweis}

• _{(408) Kernel Information Embeddings by Roland Memisevic}
• _{(263) Block-Quantized Kernel Matrix for Fast Spectral Embedding by Kai Zhang, James Kwok}
• _{(677) Local Fisher Discriminant Analysis for Supervised Dimensionality Reduction by Masashi Sugiyama}
• _{(694) Robust Euclidean Embedding by Lawrence Cayton, Sanjoy Dasgupta}

_{RL 4: Games and Dynamic Systems
Chair: Michael Bowling}

• _{(401) Reinforcement Learning for Optimized Trade Execution by Yuriy Nevmyvaka, Yi Feng, Michael Kearns}
• _{(560) Learning Algorithms for Online Principal-Agent Problems (and Selling Goods Online) by Vincent Conitzer, Nikesh Garera}
• _{(316) Kernel Predictive Linear Gaussian Models for Nonlinear Stochastic Dynamical Systems by David Wingate, Satinder Singh}
• _{(312) Predictive Linear-Gaussian Models of Controlled Stochastic Dynamical Systems by Matthew Rudary, Satinder Singh}

_{Structured Outputs 2
Chair: Andrew McCallum}

• _{(443) Kernelizing the Output of Tree-Based Methods by Pierre Geurts, Louis Wehenkel, Florence d'Alché-Buc}
• _{(261) Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods by Vishwanathan S.V.N., Nicol Schraudolph, Mark Schmidt, Kevin Murphy}
• _{(386) Cost-Sensitive Learning with Conditional Markov Networks by Prithviraj Sen, Lise Getoor}
• _{(578) Discriminative Unsupervised Learning of Structured Predictors by Linli Xu, Dana Wilkinson, Finnegan Southey, Dale Schuurmans}

_{Scalability 1
Chair: Dragos Margineantu}

• _{(355) Sequential update of ADtrees by Josep Roure, Andrew W. Moore}
• _{(593) Fast Particle Smoother: If I Had a Million Particles by Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang}
• _{(545) Efficient MAP approximation for dense energy functions by Marius Leordeanu, Martial Hebert}
• _{(451) Cover Trees for Nearest Neighbor by Alina Beygelzimer, Sham Kakade, John Langford}

_{Clustering 4: Supervision
Chair: Chris Ding}

• _{(384) Null space versus orthogonal linear discriminant analysis by Jieping Ye, Tao Xiong}
• _{(90) Discriminative Cluster Analysis by Fernando De la Torre, Takeo Kanade}
• _{(303) Practical Solutions to the Problem of Diagonal Dominance in Kernel Document Clustering by Derek Greene, Padraig Cunningham}
• _{(155) Locally Adaptive Classification Piloted by Uncertainty by Juan Dai, Shuicheng Yan, Xiaoou Tang, James Kwok}

_{David Haussler, University of California, Santa Cruz: Ultraconserved elements, living fossil transposons, and rapid bursts of change: reconstructing the uneven evolutionary history of the human genome}

_{Note: Plenary talks will be held in the McConomy Auditorium}

_{RL 5: More New Algorithms
Chair: Nando de Freitas}

• _{(585) Maximum Margin Planning by Nathan Ratliff, James Bagnell, Martin Zinkevich}
• _{(517) Automatic basis function construction for approximate dynamic programming and reinforcement learning by Philipp Keller, Shie Mannor, Doina Precup}
• _{(503) Region-Based Value Iteration for Partially Observable Markov Decision Processes by Hui Li, Xuejun Liao, Lawrence Carin}
• _{(375) Probabilistic inference for solving discrete and continuous state Markov Decision Processes by Marc Toussaint, Amos Storkey}

_{Learning and Modeling Graph Structure
Chair: Alice Zheng}

• _{(57) Bayesian Learning of Measurement and Structural Models by Ricardo Silva, Richard Scheines}
• _{(122) Full Bayesian Network Classifiers by Jiang Su, Harry Zhang}
• _{(72) Clustering Graphs by Weighted Substructure Mining by Koji Tsuda, Taku Kudo}
• _{(559) Graph Model Selection using Maximum Likelihood by Ivona Bezakova, Adam Kalai, Rahul Santhanam}

_{Feature Selection and Weighting
Chair: Stan Matwin}

• _{(39) Iterative RELIEF for Feature Weighting by Yijun Sun, Jian Li}
• _{(172) Efficient Lazy Elimination for Averaged One-Dependence Estimators by Fei Zheng, Geoffrey Webb}
• _{(338) Feature Value Acquisition in Testing: A Sequential Batch Test Algorithm by Shengli Sheng, Charles X. Ling}
• _{(350) Nightmare at test time: Robust learning by feature deletion by Amir Globerson, Sam Roweis}

_{Semi-Supervised Learning 1
Chair: Rayid Ghani}

• _{(366) A Continuation Method for Semi-Supervised SVMs by Olivier Chapelle, Mingmin Chi, Alexander Zien}
• _{(266) Semi-Supervised Learning for Structured Output Variables by Ulf Brefeld, Tobias Scheffer}
• _{(211) An Analysis of Graph Cut Size for Transductive Learning by Steve Hanneke}
• _{(60) Label Propagation Through Linear Neighborhoods by Fei Wang, Changshui Zhang}

_{RL 6: New Representations
Chair: Michael Littman}

• _{(603) Looping Suffix Tree-Based Inference of Partially Observable Hidden State by Michael Holmes, Charles Isbell}
• _{(260) Learning the Structure of Factored Markov Decision Processes in Reinforcement Learning Problems by Thomas Degris, Olivier Sigaud, Pierre-Henri Wuillemin}
• _{(642) Relational Temporal Difference Learning by Nima Asgharbeygi, David Stracuzzi, Pat Langley}
• _{(549) Learning Hierarchical Task Networks by Observation by Negin Nejati, Pat Langley, Tolga Konik}

_{Novel Approaches 1
Chair: Marko Grobelnik}

• _{(620) MISSL: Multiple-Instance Semi-Supervised Learning by Rouhollah Rahmani, Sally Goldman}
• _{(597) A Regularization Framework for Multiple-Instance Learning by Pak-Ming Cheung, James T. Kwok}
• _{(477) Nonstationary Kernel Combination by Darrin Lewis, Tony Jebara, William Noble}
• _{(302) Predictive Search Distributions by Edwin Bonilla, Chris Williams, Felix Agakov, John Cavazos, John Thomson, Michael O'Boyle}

_{Scalability 2: Kernel Methods
Chair: Gert Lankriet}

• _{(486) Fast and Space Efficient String Kernels using Suffix Arrays by Choon Hui Teo, Vishwanathan S.v.n}
• _{(508) Concept Boundary Detection for Speeding up SVMs by Navneet Panda, Edward Y. Chang, Gang Wu}
• _{(433) Fast Transpose Methods for Kernel Learning on Sparse Data by Patrick Haffner}
• _{(468) Trading Convexity for Scalability by Ronan Collobert, Fabian Sinz, Jason Weston, Leon Bottou}

_{Semi-Supervised Learning 2
Chair: Jerry Zhu}

• _{(463) Inference with the Universum by Jason Weston, Ronan Collobert, Fabian Sinz, Leon Bottou, Vladimir Vapnik}
• _{(661) Higher Order Learning with Graphs by Sameer Agarwal, Kristin Branson, Serge Belongie}
• _{(246) Ranking on Graph Data by Shivani Agarwal}
• _{(372) Semi-Supervised Nonlinear Dimensionality Reduction by Xin Yang, Haoying Fu, Hongyuan Zha, Jesse Barlow}

_{Bayesian Inference and Topic Models
Chair: Yee Whye Teh}

• _{(97) A Note on Mixtures of Experts for Multiclass Responses: Approximation Rate and Consistent Bayesian Inference by Yang Ge, Wenxin Jiang}
• _{(177) A Choice Model with Infinitely Many Latent Features by Dilan Gorur, Frank Jaekel, Carl Edward Rasmussen}
• _{(490) Pachinko Allocation: DAG-Structured Mixture Models of Topic Correlations by Wei Li, Andrew McCallum}
• _{(504) Dynamic Topic Models by David Blei, John Lafferty}

_{Novel Approaches 2
Chair: Eric Xing}

• _{(548) Quadratic Programming Relaxations for Metric Labeling and Markov Random Field MAP Estimation by Pradeep Ravikumar, John Lafferty}
• _{(77) Ranking Individuals by Group Comparisons by Tzu-Kuo Huang, Chih-Jen Lin, Ruby Chiu-Hsing Weng}
• _{(130) Learning High-Order MRF Priors of Color Images by Julian McAuley, Tiberio Caetano, Alex Smola, Matthias Franz}
• _{(447) Constructing Informative Priors using Transfer Learning by Rajat Raina, Andrew Ng, Daphne Koller}

_{Learning for Time Series
Chair: Tony Jebara}

• _{(440) Combining Discriminative Features to Infer Complex Trajectories by David Ross, Simon Osindero, Richard Zemel}
• _{(495) Fast Time Series Classification Using Numerosity Reduction by Xiaopeng Xi, Eamonn Keogh, Christian Shelton, Li Wei, Chotirat Ann Ratanamahatana}
• _{(307) Data Association for Topic Intensity Tracking by Andreas Krause, Jure Leskovec, Carlos Guestrin}
• _{(522) Hidden Process Models by Rebecca Hutchinson, Tom Mitchell, Indrayana Rustandi}

_{Clustering 5
Chair: Jennifer Dy}

• _{(150) Spectral Clustering for Multi-type Relattional Data by Bo Long, Mark Zhang, Xiaoyun Wu, Philip S. Yu}
• _{(471) Combined Central and Subspace Clustering for Computer Vision Applications by Le Lu, Rene Vidal}
• _{(164) An Investigation of Computational and Informational Limits in Gaussian Mixture Clustering by Nathan Srebro, Gregory Shakhnarovich, Sam Roweis}
• _{(452) A New Approach to Data Driven Clustering by Arik Azran, Zoubin Ghahramani}