| 2026 | COLT | Learning to Reason with Curriculum I: Provable Benefits of Autocurriculum. | Nived Rajaraman, Audrey Huang, Miro Dudk, Robert E. Schapire, Dylan J. Foster, Akshay Krishnamurthy |
| 2025 | COLT | On the Hardness of Bandit Learning. | Nataly Brukhim, Aldo Pacchiano, Miroslav Dudk, Robert E. Schapire |
| 2024 | AISTATS | Lexicographic Optimization: Algorithms and Stability. | Jacob D. Abernethy, Robert E. Schapire, Umar Syed |
| 2024 | ICML | Provable Interactive Learning with Hindsight Instruction Feedback. | Dipendra Misra, Aldo Pacchiano, Robert E. Schapire |
| 2021 | ICML | Interactive Learning from Activity Description. | Khanh Nguyen, Dipendra Misra, Robert E. Schapire, Miroslav Dudk, Patrick Shafto |
| 2021 | STOC | Contextual search in the presence of irrational agents. | Akshay Krishnamurthy, Thodoris Lykouris, Chara Podimata, Robert E. Schapire |
| 2020 | ALT | Interactive Learning of a Dynamic Structure. | Ehsan Emamjomeh-Zadeh, David Kempe, Mohammad Mahdian, Robert E. Schapire |
| 2020 | COLT | Gradient descent follows the regularization path for general losses. | Ziwei Ji, Miroslav Dudk, Robert E. Schapire, Matus Telgarsky |
| 2019 | FOCS | Adversarial Bandits with Knapsacks. | Nicole Immorlica, Karthik Abinav Sankararaman, Robert E. Schapire, Aleksandrs Slivkins |
| 2018 | ALT | Robust Inference for Multiclass Classification. | Uriel Feige, Yishay Mansour, Robert E. Schapire |
| 2018 | ICML | Practical Contextual Bandits with Regression Oracles. | Dylan J. Foster, Alekh Agarwal, Miroslav Dudk, Haipeng Luo, Robert E. Schapire |
| 2018 | ICML | Learning Deep ResNet Blocks Sequentially using Boosting Theory. | Furong Huang, Jordan T. Ash, John Langford, Robert E. Schapire |
| 2017 | COLT | Open Problem: First-Order Regret Bounds for Contextual Bandits. | Alekh Agarwal, Akshay Krishnamurthy, John Langford, Haipeng Luo, Robert E. Schapire |
| 2017 | COLT | Corralling a Band of Bandit Algorithms. | Alekh Agarwal, Haipeng Luo, Behnam Neyshabur, Robert E. Schapire |
| 2017 | FOCS | Oracle-Efficient Online Learning and Auction Design. | Miroslav Dudk, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan |
| 2017 | ICML | Contextual Decision Processes with low Bellman rank are PAC-Learnable. | Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire |
| 2016 | COLT | Instance-dependent Regret Bounds for Dueling Bandits. | Akshay Balsubramani, Zohar S. Karnin, Robert E. Schapire, Masrour Zoghi |
| 2016 | ICML | Efficient Algorithms for Adversarial Contextual Learning. | Vasilis Syrgkanis, Akshay Krishnamurthy, Robert E. Schapire |
| 2015 | COLT | Contextual Dueling Bandits. | Miroslav Dudk, Katja Hofmann, Robert E. Schapire, Aleksandrs Slivkins, Masrour Zoghi |
| 2015 | COLT | Learning and inference in the presence of corrupted inputs. | Uriel Feige, Yishay Mansour, Robert E. Schapire |
| 2015 | COLT | Achieving All with No Parameters: AdaNormalHedge. | Haipeng Luo, Robert E. Schapire |
| 2015 | IJCAI | Collaborative Place Models. | Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara |
| 2014 | AISTATS | Collaborative Ranking for Local Preferences. | Berk Kapicioglu, David S. Rosenberg, Robert E. Schapire, Tony Jebara |
| 2014 | COLT | Robust Multi-objective Learning with Mentor Feedback. | Alekh Agarwal, Ashwinkumar Badanidiyuru, Miroslav Dudk, Robert E. Schapire, Aleksandrs Slivkins |
| 2014 | ICASSP | Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware. | Zhuo Wang, Robert E. Schapire, Naveen Verma |
| 2014 | ICML | Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. | Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire |
| 2014 | ICML | Towards Minimax Online Learning with Unknown Time Horizon. | Haipeng Luo, Robert E. Schapire |
| 2011 | ICASSP | Compressive sensing meets game theory. | Sina Jafarpour, Robert E. Schapire, Volkan Cevher |
| 2011 | ISIT | A game theoretic approach to expander-based compressive sensing. | Sina Jafarpour, Volkan Cevher, Robert E. Schapire |
| 2010 | COLT | The Convergence Rate of AdaBoost. | Robert E. Schapire |
| 2010 | WWW | A contextual-bandit approach to personalized news article recommendation. | Lihong Li, Wei Chu, John Langford, Robert E. Schapire |
| 2010 | UAI | Combining Spatial and Telemetric Features for Learning Animal Movement Models. | Berk Kapicioglu, Robert E. Schapire, Martin Wikelski, Tamara Broderick |
| 2008 | ALT | Learning with Continuous Experts Using Drifting Games. | Indraneel Mukherjee, Robert E. Schapire |
| 2008 | ICML | Apprenticeship learning using linear programming. | Umar Syed, Michael H. Bowling, Robert E. Schapire |
| 2008 | OSDI | From Optimization to Regret Minimization and Back Again. | Ioannis C. Avramopoulos, Jennifer Rexford, Robert E. Schapire |
| 2007 | ICML | Hierarchical maximum entropy density estimation. | Miroslav Dudk, David M. Blei, Robert E. Schapire |
| 2007 | UAI | Imitation Learning with a Value-Based Prior. | Umar Syed, Robert E. Schapire |
| 2006 | COLT | Maximum Entropy Distribution Estimation with Generalized Regularization. | Miroslav Dudk, Robert E. Schapire |
| 2006 | ICML | Algorithms for portfolio management based on the Newton method. | Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire |
| 2006 | ICML | How boosting the margin can also boost classifier complexity. | Lev Reyzin, Robert E. Schapire |
| 2005 | COLT | Margin-Based Ranking Meets Boosting in the Middle. | Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire |
| 2004 | COLT | Performance Guarantees for Regularized Maximum Entropy Density Estimation. | Miroslav Dudk, Steven J. Phillips, Robert E. Schapire |
| 2004 | COLT | Boosting Based on a Smooth Margin. | Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies |
| 2004 | ICML | A maximum entropy approach to species distribution modeling. | Steven J. Phillips, Miroslav Dudk, Robert E. Schapire |
| 2003 | ICASSP | Active learning for spoken language understanding. | Gkhan Tr, Robert E. Schapire, Dilek Hakkani-Tr |
| 2002 | ICASSP | Combining prior knowledge and boosting for call classification in spoken language dialogue. | Marie Rochery, Robert E. Schapire, Mazin G. Rahim, Narendra K. Gupta, Giuseppe Riccardi, Srinivas Bangalore, Hiyan Alshawi, Shona Douglas |
| 2002 | ICML | Incorporating Prior Knowledge into Boosting. | Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra K. Gupta |
| 2002 | ICML | Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. | Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, Jnos A. Csirik |
| 2002 | Interspeech | AT&t help desk. | Giuseppe Di Fabbrizio, Dawn Dutton, Narendra K. Gupta, Barbara Hollister, Mazin G. Rahim, Giuseppe Riccardi, Robert E. Schapire, Juergen Schroeter |
| 2002 | UAI | Advances in Boosting. | Robert E. Schapire |
| 2001 | AISTATS | Why averaging classifiers can protect against overfitting. | Yoav Freund, Yishay Mansour, Robert E. Schapire |
| 2000 | CIKM | Boosting for Document Routing. | Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal |
| 2000 | COLT | Logistic Regression, AdaBoost and Bregman Distances. | Michael Collins, Robert E. Schapire, Yoram Singer |
| 2000 | COLT | On the Convergence Rate of Good-Turing Estimators. | David A. McAllester, Robert E. Schapire |
| 2000 | ICML | Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. | Erin L. Allwein, Robert E. Schapire, Yoram Singer |
| 1999 | ALT | Theoretical Views of Boosting and Applications. | Robert E. Schapire |
| 1999 | COLT | Drifting Games. | Robert E. Schapire |
| 1999 | EMNLP | Boosting Applied to Tagging and PP Attachment. | Steven Abney, Robert E. Schapire, Yoram Singer |
| 1999 | IJCAI | A Brief Introduction to Boosting. | Robert E. Schapire |
| 1998 | COLT | Large Margin Classification Using the Perceptron Algorithm. | Yoav Freund, Robert E. Schapire |
| 1998 | COLT | Improved Boosting Algorithms using Confidence-Rated Predictions. | Robert E. Schapire, Yoram Singer |
| 1998 | ICML | An Efficient Boosting Algorithm for Combining Preferences. | Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer |
| 1998 | SIGIR | Boosting and Rocchio Applied to Text Filtering. | Robert E. Schapire, Yoram Singer, Amit Singhal |
| 1997 | ICML | Using output codes to boost multiclass learning problems. | Robert E. Schapire |
| 1997 | ICML | Boosting the margin: A new explanation for the effectiveness of voting methods. | Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee |
| 1997 | STOC | Using and Combining Predictors That Specialize. | Yoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth |
| 1996 | COLT | Game Theory, On-Line Prediction and Boosting. | Yoav Freund, Robert E. Schapire |
| 1996 | ICML | Experiments with a New Boosting Algorithm. | Yoav Freund, Robert E. Schapire |
| 1996 | ICML | On-Line Portfolio Selection Using Multiplicative Updates. | David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth |
| 1996 | SIGIR | Training Algorithms for Linear Text Classifiers. | David D. Lewis, Robert E. Schapire, James P. Callan, Ron Papka |
| 1995 | COLT | Predicting Nearly as Well as the Best Pruning of a Decision Tree. | David P. Helmbold, Robert E. Schapire |
| 1995 | COLT | A Comparison of New and Old Algorithms for a Mixture Estimation Problem. | David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth |
| 1995 | FOCS | Gambling in a Rigged Casino: The Adversarial Multi-Arm Bandit Problem. | Peter Auer, Nicol Cesa-Bianchi, Yoav Freund, Robert E. Schapire |
| 1995 | FOCS | Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries. | Yoav Freund, Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire |
| 1994 | ICML | On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. | Robert E. Schapire, Manfred K. Warmuth |
| 1994 | STOC | On the learnability of discrete distributions. | Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie |
| 1993 | COLT | Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. | Robert E. Schapire, Linda Sellie |
| 1993 | STOC | How to use expert advice. | Nicol Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth |
| 1993 | STOC | Efficient learning of typical finite automata from random walks. | Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie |
| 1992 | COLT | Toward Efficient Agnostic Learning. | Michael J. Kearns, Robert E. Schapire, Linda Sellie |
| 1991 | COLT | Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. | David Haussler, Michael J. Kearns, Robert E. Schapire |
| 1991 | COLT | Learning Probabilistic Read-Once Formulas on Product Distributions. | Robert E. Schapire |
| 1990 | COLT | On the Sample Complexity of Weak Learning. | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire |
| 1990 | COLT | Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire |
| 1990 | COLT | Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). | Michael J. Kearns, Robert E. Schapire |
| 1990 | COLT | Pattern Languages are not Learnable. | Robert E. Schapire |
| 1990 | FOCS | Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract) | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire |
| 1990 | FOCS | Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract) | Michael J. Kearns, Robert E. Schapire |
| 1989 | FOCS | Learning Binary Relations and Total Orders (Extended Abstract) | Sally A. Goldman, Ronald L. Rivest, Robert E. Schapire |
| 1989 | FOCS | The Strength of Weak Learnability (Extended Abstract) | Robert E. Schapire |
| 1989 | STOC | Inference of Finite Automata Using Homing Sequences (Extended Abstract) | Ronald L. Rivest, Robert E. Schapire |
| 1987 | FOCS | Diversity-Based Inference of Finite Automata (Extended Abstract) | Ronald L. Rivest, Robert E. Schapire |