| Exploration and Apprenticeship Learning in Reinforcement Learning |
| Pieter Abbeel, Andrew Y. Ng |
| Active Learning for Hidden Markov Models: Objective Functions and Algorithms |
| Brigham Anderson, Andrew Moore |
| Tempering for Bayesian C&RT |
| Nicos Angelopoulos, James Cussens |
| Fast Condensed Nearest Neighbor Rule |
| Fabrizio Angiulli |
| Predictive low-rank decomposition for kernel methods |
| Francis R. Bach, Michael I. Jordan |
| Multi-Way Distributional Clustering via Pairwise Interactions |
| Ron Bekkerman, Ran El-Yaniv, Andrew McCallum |
| Error Limiting Reductions Between Classification Tasks |
| Alina Beygelzimer, Varsha Dani, Tom Hayes, John Langford, Bianca Zadrozny |
| Multi-Instance Tree Learning |
| Hendrik Blockeel, David Page, Ashwin Srinivasan |
| Action Respecting Embedding |
| Michael Bowling, Ali Ghodsi, Dana Wilkinson |
| Clustering Through Ranking On Manifolds |
| Markus Breitenbach, Gregory Z. Grudic |
| Reducing Overfitting in Process Model Induction |
| Will Bridewell, Narges Bani Asani, Pat Langley, Ljupco Todorovski |
| Learning to Rank using Gradient Descent |
| Chris Burges,Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, Greg Hullender |
| Learning Class-Discriminative Dynamic Bayesian Networks |
| John Burge, Terran Lane |
| Recognition and Reproduction of Gestures using a Probabilistic Framework combining PCA, ICA and HMM |
| Sylvain Calinon, Aude Billard |
| Predicting Probability Distributions for Surf Height Using an Ensemble of Mixture Density Networks |
| Michael Carney, Padraig Cunningham, Jim Dowling, Ciaran Lee |
| Hedged learning: Regret minimization with learning experts |
| Yu-Han Chang, Leslie Kaelbling |
| Variational Bayesian Image Modelling |
| Li Cheng, Feng Jiao, Dale Schuurmans, Shaojun Wang |
| Preference Learning with Gaussian Processes |
| Wei Chu, Zoubin Ghahramani |
| New Approaches to Support Vector Ordinal Regression |
| Wei Chu, S. Sathiya Keerthi |
| A General Regression Technique for Learning Transductions |
| Corinna Cortes, Mehryar Mohri, Jason Weston |
| Learning to Compete, Compromise, and Cooperate in Repeated General-Sum Games |
| Jacob W. Crandal, Michael A. Goodrich |
| Learning as Search Optimization: Approximate Large Margin Methods for Structured Prediction |
| Hal Daume III, Daniel Marcu |
| Multimodal Oriented Discriminant Analysis |
| Fernando De la Torre, Takeo Kanade |
| A Practical Generalization of Fourier-based Learning |
| Adam Drake, Dan Ventura |
| Combining Model-Based and Instance-Based Learning for First Order Regression |
| Kurt Driessens, Saso Dzeroski |
| Reinforcement learning with Gaussian processes |
| Yaakov Engel, Shie Mannor, Ron Meir |
| Experimental Comparison between Bagging and Monte Carlo Ensemble Classification |
| Roberto Esposito, Lorenza Saitta |
| Supervised Clustering with Support Vector Machines |
| Thomas Finley, Thorsten Joachims |
| Optimal Assignment Kernels For Attributed Molecular Graphs |
| Holger Fröhlich, Jörg Wegner, Florian Sieker, Andreas Zell |
| Closed-form dual perturb and combine for tree-based models |
| Pierre Geurts, Louis Wehenkel |
| Hierarchic Bayesian Models for Kernel Learning |
| Mark Girolami, Simon Rogers |
| Online Feature Selection for Pixel Classification |
| Karen Glocer, Damian Eads, James Theiler |
| Learning Strategies for Story Comprehension: A Reinforcement Learning Approach |
| Eugene Grois, David C. Wilkins |
| Near-Optimal Sensor Placements in Gaussian Processes |
| Carlos Guestrin, Andreas Krause, Ajit Pauk Singh |
| Robust One-Class Clustering Using Hybrid Global and Local Search |
| Gunjan Gupta, Joydeep Ghosh |
| Statistical and Computational Analysis of Locality Preserving Projection |
| Xiaofei He, Deng Cai, Wanli Min |
| Intrinsic Dimensionality Estimation of Submanifolds in (d |
| Matthias Hein, Jean-Yves Audibert |
| Bayesian Hierarchical Clustering |
| Katherine Heller, Zoubin Ghahramani |
| Online Learning over Graphs |
| Mark Herbster, Massimiliano Pontil, Lisa Wainer |
| Adapting Two-Class Classification Methods to Many Class Problems |
| Simon I. Hill, Arnaud Doucet |
| A Martingale Framework for Concept Change Detection in Time-Varying Data Streams |
| Shen-Shyang Ho |
| Multi-Class protein fold detection using adaptive codes |
| Eugene Ie, Jason Weston, William Stafford Noble, Christina Leslie |
| Learning Approximate Preconditions for Methods in Hierarchical Plans |
| Okhtay Ilghami, Héctor Muñoz-Avila, Dana S. Nau, David W. Aha |
| Evaluating Machine Learning for Information Extraction |
| Neil Ireson, Fabio Ciravegna, Mary Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli |
| Learn to Weight Terms in Information Retrieval Using Category Information |
| Rong Jin, Joyce Y. Chai, Luo Si |
| A Smoothed Boosting Algorithm Using Probabilistic Output Codes |
| Rong Jin, Jian Zhang |
| Efficient discriminative learning of Bayesian network classifier via Boosted Augmented Naive Bayes |
| Yushi Jing, Vladimir Pavlovic, James M. Rehg |
| A Support Vector Method for Multivariate Performance Measures |
| Thorsten Joachims |
| Error Bounds for Correlation Clustering |
| Thorsten Joachims, John Hopcroft |
| Interactive Learning of Mappings from Visual Percepts to Actions |
| Sébastien Jodogne, Justus H. Piater |
| A Causal Approach to Hierarchical Decomposition of Factored MDPs |
| Anders Jonsson, Andrew Barto |
| A Comparison of Tight Generalization Error Bounds |
| Matti Kääriäinen, John Langford |
| Generalized LARS as an Effective Feature Selection Tool for Text Classification With SVMs |
| S. Sathiya Keerthi |
| Ensembles of Biased Classifiers |
| Rinat Khoussainov, Andreas Hess, Nicholas Kushmerick |
| Computational Aspects of Bayesian Partition Models |
| Mikko Koivisto, Kismat Sood |
| Learning the Structure of Markov Logic Networks |
| Stanley Kok, Pedro Domingos |
| Using Additive Expert Ensembles to Cope with Concept Drift |
| Jeremy Kolter, Marcus Maloof |
| Semi-supervised Graph Clustering: A Kernel Approach |
| Brian Kulis, Sugato Basu, Inderjit S. Dhillon, Raymond J. Mooney |
| A Brain Computer Interface with Online Feedback based on Magnetoencephalography |
| Thomas Navin Lal, Michael Schröder, N. Jeremy Hill, Hubert Preissl, Thilo Hinterberger, Jürgen Mellinger, Martin Bogdan, Wolfgang Rosenstiel, Thomas, Hofmann, Niels Birbaumer, Bernhard Schölkopf |
| Relating Reinforcement Learning Performance to Classification Performance |
| John Langford, Bianca Zadrozny |
| PAC-Bayes Risk Bounds for Sample-Compressed Gibbs Classifiers |
| François Laviolette, Mario Marchand |
| Heteroscedastic Gaussian Process Regression |
| Quoc V. Le, Alex J. Smola, Stéphane Canu |
| Predicting Relative Performance of Classifiers from Samples |
| Rui Leite, Pavel Brazdil |
| Logistic Regression with an Auxiliary Data Source |
| Xuejun Liao, Ya Xue, Lawrence Carin |
| Predicting Protein Folds with Structural Repeats Using a Chain Graph Model |
| Yan Liu, Eric Xing, Jaime Carbonell |
| Unsupervised Evidence Integration |
| Philip M. Long, Vinay Varadan, Sarah Gilman, Mark Treshock, Rocco A. Servedio |
| Naive Bayes Models for Probability Estimation |
| Daniel Lowd, Pedro Domingos |
| ROC Confidence Bands : An Empirical Evaluation |
| Sofus A. Macskassy, Foster Provost, Saharon Rosset |
| Modeling Word Burstiness Using the Dirichlet Distribution |
| Rasmus Madsen, David Kauchak, Charles Elkan |
| Proto-Value Functions: Developmental Reinforcement Learning |
| Sridhar Mahadeva |
| The cross entropy method for classification |
| Shie Mannor, Dori Peleg, Reuven Y. Rubinstein |
| Bounded Real-Time Dynamic Programming: RTDP with monotone upper bounds and performance guarantees |
| H. Brendan McMahan, Maxim Likhachev, Geoffrey J. Gordon |
| Comparing Clusterings - An Axiomatic View |
| Marina Meila |
| Weighted Decomposition Kernels |
| Sauro Menchetti, Fabrizio Costa, Paolo Frasconi |
| High Speed Obstacle Avoidance using Monocular Vision and Reinforcement learning |
| Jeff Michels, Ashutosh Saxena, Andrew Y. Ng |
| Dynamic Preferences in Multi-Criteria Reinforcement Learning |
| Sriraam Natarajan, Prasad Tadepalli |
| Learning First-Order Probabilistic Models with Combining Rules |
| Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo Restificar |
| An Efficient Method for Simplifying Support Vector Machines |
| DucDung Nguyen, Tu Bao Ho |
| Predicting Good Probabilities With Supervised Learning |
| Alexandru Niculescu-Mizil, Rich Caruana |
| Recycling Data for Multi-Agent Learning |
| Santiago Ontañon, Enric Plaza |
| A Graphical Model for Chord Progressions Embedded in a Psychoacoustic Space |
| Jean-François Paiement, Douglas Eck, Samy Bengio, David Barber |
| Q-Learning of Sequential Attention for Visual Object Recognition from Informative Local Descriptors |
| Lucas Paletta, Gerald Fritz, Christin Seifert |
| Discriminative versus Generative Parameter and Structure Learning of Bayesian Network Classifiers |
| Franz Pernkopf, Jeff Bilmes |
| Optimizing Abstaining Classifiers using ROC Analysis |
| Tadeusz Pietraszek |
| Independent Subspace Analysis Using Geodesic Spanning Trees |
| Barnabas Poczos, András Lörincz |
| A Model for Handling Approximate, Noisy or Incomplete Labeling in Text Classification |
| Ganesh Ramakrishnan, Krishna Prasad Chitrapura, Raghu Krishnapuram, Pushpak Bhattacharyya |
| Healing the Relevance Vector Machine through Augmentation |
| Carl Edward Rasmussen, Joaquin Quinonero-Candela |
| Supervised versus Multiple Instance Learning: An Empirical Comparison |
| Soumya Ray, Mark Craven |
| Generalized Skewing for Functions with Continuous and Nominal Attributes |
| Soumya Ray, David Page |
| Fast Maximum Margin Matrix Factorization for Collaborative Prediction |
| Jason D. M. Rennie, Nati Srebro |
| Coarticulation: An Approach for Generating Concurrent Plans in Markov Decision Processes |
| Khashayar Rohanimanesh, Sridhar Mahadevan |
| Why Skewing Works: Learning Difficult Boolean Functions with Greedy Tree Learners |
| Bernard Rosell, Lisa Hellerstein, Soumya Ray, David Page |
| Integer Linear Programming Inference for Conditional Random Fields |
| Dan Roth, Wen-Tau Yih |
| Learning Hierarchical Multi-Category Text Classification Models |
| Juho Rousu, Craig Saunders, Sandor Szedmak, John Shawe-Taylor |
| Expectation Maximization Algorithms for Conditional Likelihoods |
| Jarkko Salojärvi, Kai Puolamäki, Samuel Kaski |
| Estimating and computing density based distance metrics |
| Sajama, Alon Orlitsky |
| Supervised dimensionality reduction using mixture models |
| Sajama, Alon Orlitsky |
| Object Correspondence as a Machine Learning Problem |
| Bernhard Schölkopf, Florian Steinke, Volker Blanz |
| Analysis and Extension of Spectral Methods for Nonlinear Dimensionality Reduction |
| Fei Sha, Lawrence K. Saul |
| Non-Negative Tensor Factorization with Applications to Statistics and Computer Vision |
| Amnon Shashua, Tamir Hazan |
| Fast Inference and Learning in Large-State-Space HMMs |
| Sajid M. Siddiqi, Andrew W. Moore |
| New D-Separation Identification Results for Learning Continuous Latent Variable Models |
| Ricardo Silva, Richard Scheines |
| Identifying Useful Subgoals in Reinforcement Learning by Local Graph Partitioning |
| Ozgur Simsek, Alicia Wolfe, Andrew Barto |
| Beyond the Point Cloud: from Transductive to Semi-supervised Learning |
| Vikas Sindhwani, Partha Niyogi, Mikhail Belkin |
| Active Learning for Sampling in Time-Series Experiments With Application to Gene Expression Analysis |
| Rohit Singh, Nathan Palmer, David Gifford, Bonnie Berger, Ziv Bar-Joseph |
| Compact approximations to Bayesian predictive distributions |
| Edward Snelson, Zoubin Ghahramani |
| Large Scale Genomic Sequence SVM Classifiers |
| Sören Sonnenburg, Gunnar Rätsch, Bernhard Schölkopf |
| A Theoretical Analysis of Model-Based Interval Estimation |
| Alexander L. Strehl, Michael L. Littman |
| Explanation-Augmented SVM: an Approach to Incorporating Domain Knowledge into SVM Learning |
| Qiang Sun, Gerald DeJong |
| Unifying the Error-Correcting and Output-Code AdaBoost within the Margin Framework |
| Yijun Sun, Sinisa Todorovic, Jian Li, Dapeng Wu |
| Finite Time Bounds for Sampling Based Fitted Value Iteration |
| Csaba Szepesvári, Rémi Munos |
| TDLambda Networks: Temporal-Difference Networks with Eligibility Traces |
| Brian Tanner, Richard Sutton |
| Learning Structured Prediction Models: A Large Margin Approach |
| Ben Taskar, Vassil Chatalbashev, Daphne Koller, Carlos Guestrin |
| Learning Discontinuities with Products-of-Sigmoids for Switching between Local Models |
| Marc Toussaint, Sethu Vijayakumar |
| Core Vector Regression for Very Large Regression Problems |
| Ivor W. Tsang, James T. Kwok, Kimo T. Lai |
| Propagating Distributions on a Hypergraph by Dual Information Regularization |
| Koji Tsuda |
| Hierarchical Dirichlet Model for Document Classification |
| Sriharsha Veeramachaneni, Diego Sona, Paolo Avesani |
| Implicit Surface Modelling as an Eigenvalue Problem |
| Christian Walder, Olivier Chapelle, Bernhard Schölkopf |
| New Kernels for Protein Structural Motif Discovery and Function Classification |
| Chang Wang, Stephen Scott |
| Exploiting Syntactic, Semantic and Lexical Regularities in Language Modeling via Directed Markov Random Fields |
| Shaojun Wang, Shaomin Wang, Russell Greiner, Dale Schuurmans, Li Cheng |
| Bayesian Sparse Sampling for On-line Reward Optimization |
| Tao Wang, Daniel J. Lizotte, Michael Bowling, Dale Schuurmans |
| Learning Predictive Representations from a History |
| Eric Wiewiora |
| Incomplete-Data Classification using Logistic Regression |
| David Williams, Xuejun Liao, Ya Xue, Lawrence Carin |
| Learning Predictive State Representations in Dynamical Systems Without Reset |
| Britton Wolfe, Michael R. James, Satinder Singh |
| Linear Asymmetric Classifier for Cascade Detectors |
| Jianxin Wu, Matthew D. Mullin, James M. Rehg |
| Building Sparse Large Margin Classifiers |
| Mingrui Wu, Bernhard Schölkopf, Goekhan Bakir |
| Dirichlet Enhanced Relational Learning |
| Zhao Xu, Volker Tresp, Kai Yu, Shipeng Yu, Hans-Peter Kriegel |
| Learning Gaussian Processes from Multiple Tasks |
| Kai Yu, Volker Tresp, Anton Schwaighofer |
| Augmenting Naive Bayes for Ranking |
| Harry Zhang, Liangxiao Jiang, Jiang Su |
| A New Mallows Distance Based Metric For Comparing Clusterings |
| Ding Zhou, Jia Li, Hongyuan Zha |
| Learning from Labeled and Unlabeled Data on a Directed Graph |
| Dengyong Zhou, Jiayuan Huang, Bernhard Schölkopf |
| 2D Conditional Random Fields for Web Information Extraction |
| Jun Zhu, Zaiqing Nie, Ji-Rong Wen, Bo Zhang, Wei-Ying Ma |
| Harmonic mixtures: combining mixture models and graph-based methods for inductive and scalable semi-supervised learning |
| Xiaojin Zhu, John Lafferty |
| Large Margin Non-Linear Embedding |
| Alexander Zien, Joaquin Quinonero-Candela |
