Schapire, Robert E.

103 publications

NeurIPS 2025 Efficient and Near-Optimal Algorithm for Contextual Dueling Bandits with Offline Regression Oracles Aadirupa Saha, Robert E. Schapire

ICML 2024 Provable Interactive Learning with Hindsight Instruction Feedback Dipendra Misra, Aldo Pacchiano, Robert E. Schapire

NeurIPS 2023 A Unified Model and Dimension for Interactive Estimation Nataly Brukhim, Miro Dudik, Aldo Pacchiano, Robert E. Schapire

NeurIPS 2022 Provably Sample-Efficient RL with Side Information About Latent Dynamics Yao Liu, Dipendra Misra, Miro Dudik, Robert E. Schapire

NeurIPS 2021 Bayesian Decision-Making Under Misspecified Priors with Applications to Meta-Learning Max Simchowitz, Christopher Tosh, Akshay Krishnamurthy, Daniel J. Hsu, Thodoris Lykouris, Miro Dudik, Robert E. Schapire

NeurIPS 2021 Multiclass Boosting and the Cost of Weak Learning Nataly Brukhim, Elad Hazan, Shay Moran, Indraneel Mukherjee, Robert E. Schapire

COLT 2020 Gradient Descent Follows the Regularization Path for General Losses Ziwei Ji, Miroslav Dudík, Robert E. Schapire, Matus Telgarsky

ALT 2020 Interactive Learning of a Dynamic Structure Ehsan Emamjomeh-Zadeh, David Kempe, Mohammad Mahdian, Robert E. Schapire

NeurIPS 2019 Reinforcement Learning with Convex Constraints Sobhan Miryoosefi, Kianté Brantley, Hal Daume Iii, Miro Dudik, Robert E. Schapire

NeurIPS 2018 On Oracle-Efficient PAC RL with Rich Observations Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

ALT 2018 Robust Inference for Multiclass Classification Uriel Feige, Yishay Mansour, Robert E. Schapire

ICML 2017 Contextual Decision Processes with Low Bellman Rank Are PAC-Learnable Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

COLT 2017 Corralling a Band of Bandit Algorithms Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire

COLT 2017 Open Problem: First-Order Regret Bounds for Contextual Bandits Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire

NeurIPS 2016 Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire

COLT 2016 Instance-Dependent Regret Bounds for Dueling Bandits Akshay Balsubramani, Zohar S. Karnin, Robert E. Schapire, Masrour Zoghi

COLT 2015 Achieving All with No Parameters: AdaNormalHedge Haipeng Luo, Robert E. Schapire

IJCAI 2015 Collaborative Place Models Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara

COLT 2015 Contextual Dueling Bandits Miroslav Dudík, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi

NeurIPS 2015 Efficient and Parsimonious Agnostic Active Learning Tzu-Kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford, Robert E. Schapire

NeurIPS 2015 Fast Convergence of Regularized Learning in Games Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire

COLT 2015 Learning and Inference in the Presence of Corrupted Inputs Uriel Feige, Yishay Mansour, Robert E. Schapire

NeurIPS 2014 A Drifting-Games Analysis for Online Learning and Applications to Boosting Haipeng Luo, Robert E. Schapire

AISTATS 2014 Collaborative Ranking for Local Preferences Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara

COLT 2014 Robust Multi-Objective Learning with Mentor Feedback Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudík, Robert E. Schapire, Aleksandrs Slivkins

JMLR 2013 A Theory of Multiclass Boosting Indraneel Mukherjee, Robert E. Schapire

JMLR 2013 The Rate of Convergence of AdaBoost Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire

COLT 2012 Open Problem: Does AdaBoost Always Cycle? Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies

COLT 2011 The Rate of Convergence of Adaboost Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire

NeurIPS 2010 A Reduction from Apprenticeship Learning to Classification Umar Syed, Robert E. Schapire

NeurIPS 2010 A Theory of Multiclass Boosting Indraneel Mukherjee, Robert E. Schapire

UAI 2010 Combining Spatial and Telemetric Features for Learning Animal Movement Models Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick

NeurIPS 2010 Non-Stochastic Bandit Slate Problems Satyen Kale, Lev Reyzin, Robert E. Schapire

COLT 2010 The Convergence Rate of AdaBoost Robert E. Schapire

JMLR 2009 Margin-Based Ranking and an Equivalence Between AdaBoost and RankBoost Cynthia Rudin, Robert E. Schapire

ICML 2008 Apprenticeship Learning Using Linear Programming Umar Syed, Michael H. Bowling, Robert E. Schapire

ALT 2008 Learning with Continuous Experts Using Drifting Games Indraneel Mukherjee, Robert E. Schapire

MLJ 2008 On Reoptimizing Multi-Class Classifiers Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire, N. V. Vinodchandran

NeurIPS 2007 A Game-Theoretic Approach to Apprenticeship Learning Umar Syed, Robert E. Schapire

NeurIPS 2007 FilterBoost: Regression and Classification on Large Datasets Joseph K. Bradley, Robert E. Schapire

ICML 2007 Hierarchical Maximum Entropy Density Estimation Miroslav Dudík, David M. Blei, Robert E. Schapire

UAI 2007 Imitation Learning with a Value-Based Prior Umar Syed, Robert E. Schapire

AISTATS 2007 Maximum Entropy Correlated Equilibria Luis E. Ortiz, Robert E. Schapire, Sham M. Kakade

JMLR 2007 Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling Miroslav Dudík, Steven J. Phillips, Robert E. Schapire

ICML 2006 Algorithms for Portfolio Management Based on the Newton Method Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire

ICML 2006 How Boosting the Margin Can Also Boost Classifier Complexity Lev Reyzin, Robert E. Schapire

COLT 2006 Maximum Entropy Distribution Estimation with Generalized Regularization Miroslav Dudík, Robert E. Schapire

NeurIPS 2005 Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations Aurelie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire

NeurIPS 2005 Correcting Sample Selection Bias in Maximum Entropy Density Estimation Miroslav Dudík, Steven J. Phillips, Robert E. Schapire

COLT 2005 Margin-Based Ranking Meets Boosting in the Middle Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire

ICML 2004 A Maximum Entropy Approach to Species Distribution Modeling Steven J. Phillips, Miroslav Dudík, Robert E. Schapire

COLT 2004 Boosting Based on a Smooth Margin Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies

COLT 2004 Performance Guarantees for Regularized Maximum Entropy Density Estimation Miroslav Dudík, Steven J. Phillips, Robert E. Schapire

JMLR 2004 The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire

JMLR 2003 An Efficient Boosting Algorithm for Combining Preferences Yoav Freund, Raj Iyer, Robert E. Schapire, Yoram Singer

JAIR 2003 Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David A. McAllester

NeurIPS 2003 On the Dynamics of Boosting Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire

UAI 2002 Advances in Boosting Robert E. Schapire

ICML 2002 Incorporating Prior Knowledge into Boosting Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta

MLJ 2002 Logistic Regression, AdaBoost and Bregman Distances Michael Collins, Robert E. Schapire, Yoram Singer

ICML 2002 Modeling Auction Price Uncertainty Using Boosting-Based Conditional Density Estimation Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik

NeurIPS 2001 A Generalization of Principal Components Analysis to the Exponential Family Michael Collins, S. Dasgupta, Robert E. Schapire

MLJ 2001 Drifting Games Robert E. Schapire

AISTATS 2001 Why Averaging Classifiers Can Protect Against Overfitting Yoav Freund, Yishay Mansour, Robert E. Schapire

MLJ 2000 BoosTexter: A Boosting-Based System for Text Categorization Robert E. Schapire, Yoram Singer

COLT 2000 Logistic Regression, AdaBoost and Bregman Distances Michael Collins, Robert E. Schapire, Yoram Singer

COLT 2000 On the Convergence Rate of Good-Turing Estimators David A. McAllester, Robert E. Schapire

JMLR 2000 Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers Erin L. Allwein, Robert E. Schapire, Yoram Singer

ICML 2000 Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers Erin L. Allwein, Robert E. Schapire, Yoram Singer

IJCAI 1999 A Brief Introduction to Boosting Robert E. Schapire

COLT 1999 Drifting Games Robert E. Schapire

MLJ 1999 Improved Boosting Algorithms Using Confidence-Rated Predictions Robert E. Schapire, Yoram Singer

MLJ 1999 Large Margin Classification Using the Perceptron Algorithm Yoav Freund, Robert E. Schapire

JAIR 1999 Learning to Order Things William W. Cohen, Robert E. Schapire, Yoram Singer

ALT 1999 Theoretical Views of Boosting and Applications Robert E. Schapire

ICML 1998 An Efficient Boosting Algorithm for Combining Preferences Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer

COLT 1998 Improved Boosting Algorithms Using Confidence-Rated Predictions Robert E. Schapire, Yoram Singer

COLT 1998 Large Margin Classification Using the Perceptron Algorithm Yoav Freund, Robert E. Schapire

MLJ 1997 A Comparison of New and Old Algorithms for a Mixture Estimation Problem David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth

ICML 1997 Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee

NeurIPS 1997 Learning to Order Things William W. Cohen, Robert E. Schapire, Yoram Singer

MLJ 1997 Predicting Nearly as Well as the Best Pruning of a Decision Tree David P. Helmbold, Robert E. Schapire

COLT 1997 Proceedings of the Tenth Annual Conference on Computational Learning Theory, COLT 1997, Nashville, Tennessee, USA, July 6-9, 1997 Yoav Freund, Robert E. Schapire

ICML 1997 Using Output Codes to Boost Multiclass Learning Problems Robert E. Schapire

ICML 1996 Experiments with a New Boosting Algorithm Yoav Freund, Robert E. Schapire

COLT 1996 Game Theory, On-Line Prediction and Boosting Yoav Freund, Robert E. Schapire

MLJ 1996 On the Worst-Case Analysis of Temporal-Difference Learning Algorithms Robert E. Schapire, Manfred K. Warmuth

ICML 1996 On-Line Portfolio Selection Using Multiplicative Updates David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth

COLT 1995 A Comparison of New and Old Algorithms for a Mixture Estimation Problem David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth

COLT 1995 Predicting Nearly as Well as the Best Pruning of a Decision Tree David P. Helmbold, Robert E. Schapire

MLJ 1994 Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension David Haussler, Michael J. Kearns, Robert E. Schapire

MLJ 1994 Learning Probabilistic Read-Once Formulas on Product Distributions Robert E. Schapire

ICML 1994 On the Worst-Case Analysis of Temporal-Difference Learning Algorithms Robert E. Schapire, Manfred K. Warmuth

MLJ 1994 Toward Efficient Agnostic Learning Michael J. Kearns, Robert E. Schapire, Linda Sellie

COLT 1993 Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples Robert E. Schapire, Linda Sellie

COLT 1992 Toward Efficient Agnostic Learning Michael J. Kearns, Robert E. Schapire, Linda Sellie

COLT 1991 Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension David Haussler, Michael J. Kearns, Robert E. Schapire

COLT 1991 Learning Probabilistic Read-Once Formulas on Product Distributions Robert E. Schapire

COLT 1990 Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract) Michael J. Kearns, Robert E. Schapire

COLT 1990 Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract) Sally A. Goldman, Michael J. Kearns, Robert E. Schapire

COLT 1990 On the Sample Complexity of Weak Learning Sally A. Goldman, Michael J. Kearns, Robert E. Schapire

COLT 1990 Pattern Languages Are Not Learnable Robert E. Schapire

MLJ 1990 The Strength of Weak Learnability Robert E. Schapire