Diversity-Based Inference of Finite Automata (Extended Abstract) - Ronald L. Rivest; Robert E. Schapire (FOCS, 1987)

Learning Binary Relations and Total Orders (Extended Abstract) - Sally A. Goldman; Ronald L. Rivest; Robert E. Schapire (FOCS, 1989)

The Strength of Weak Learnability (Extended Abstract) - Robert E. Schapire (FOCS, 1989)

Inference of Finite Automata Using Homing Sequences (Extended Abstract) - Ronald L. Rivest; Robert E. Schapire (STOC, 1989)

On the Sample Complexity of Weak Learning. - Sally A. Goldman; 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)

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

Pattern Languages are not Learnable. - Robert E. Schapire (COLT, 1990)

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

Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract) - Michael J. Kearns; Robert E. Schapire (FOCS, 1990)

The Strength of Weak Learnability. - Robert E. Schapire (Mach. Learn., 1990)

Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. - David Haussler; Michael J. Kearns; Manfred Opper; Robert E. Schapire (NIPS, 1991)

Design and analysis of efficient learning algorithms. - Robert E. Schapire (MIT Press, 1992)

Improving Performance in Neural Networks Using a Boosting Algorithm. - Harris Drucker; Robert E. Schapire; Patrice Y. Simard (NIPS, 1992)

Boosting Performance in Neural Networks. - Harris Drucker; Robert E. Schapire; Patrice Y. Simard (Int. J. Pattern Recognit. Artif. Intell., 1993)

Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions. - Sally A. Goldman; Michael J. Kearns; Robert E. Schapire (SIAM J. Comput., 1993)

Learning Binary Relations and Total Orders. - Sally A. Goldman; Ronald L. Rivest; Robert E. Schapire (SIAM J. Comput., 1993)

Inference of Finite Automata Using Homing Sequences - Ronald L. Rivest; Robert E. Schapire (Inf. Comput., 1993)

On the learnability of discrete distributions. - Michael J. Kearns; Yishay Mansour; Dana Ron; Ronitt Rubinfeld; Robert E. Schapire; Linda Sellie (STOC, 1994)

Diversity-Based Inference of Finite Automata. - Ronald L. Rivest; Robert E. Schapire (J. ACM, 1994)

Efficient Distribution-Free Learning of Probabilistic Concepts. - Michael J. Kearns; Robert E. Schapire (J. Comput. Syst. Sci., 1994)

Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. - David Haussler; Michael J. Kearns; Robert E. Schapire (Mach. Learn., 1994)

Toward Efficient Agnostic Learning. - Michael J. Kearns; Robert E. Schapire; Linda Sellie (Mach. Learn., 1994)

Learning Probabilistic Read-once Formulas on Product Distributions. - Robert E. Schapire (Mach. Learn., 1994)

Gambling in a Rigged Casino: The Adversarial Multi-Arm Bandit Problem. - Peter Auer; Nicolò Cesa-Bianchi; Yoav Freund; Robert E. Schapire (FOCS, 1995)

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 (FOCS, 1995)

On the Sample Complexity of Weakly Learning - Sally A. Goldman; Michael J. Kearns; Robert E. Schapire (Inf. Comput., 1995)

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

Experiments with a New Boosting Algorithm. - Yoav Freund; Robert E. Schapire (ICML, 1996)

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

Training Algorithms for Linear Text Classifiers. - David D. Lewis; Robert E. Schapire; James P. Callan; Ron Papka (SIGIR, 1996)

Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. - Robert E. Schapire; Linda Sellie (J. Comput. Syst. Sci., 1996)

On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. - Robert E. Schapire; Manfred K. Warmuth (Mach. Learn., 1996)

Using output codes to boost multiclass learning problems. - Robert E. Schapire (ICML, 1997)

Boosting the margin: A new explanation for the effectiveness of voting methods. - Robert E. Schapire; Yoav Freund; Peter Barlett; Wee Sun Lee (ICML, 1997)

Using and Combining Predictors That Specialize. - Yoav Freund; Robert E. Schapire; Yoram Singer; Manfred K. Warmuth (STOC, 1997)

Efficient Learning of Typical Finite Automata from Random Walks. - Yoav Freund; Michael J. Kearns; Dana Ron; Ronitt Rubinfeld; Robert E. Schapire; Linda Sellie (Inf. Comput., 1997)

How to use expert advice. - Nicolò Cesa-Bianchi; Yoav Freund; David Haussler; David P. Helmbold; Robert E. Schapire; Manfred K. Warmuth (J. ACM, 1997)

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. - Yoav Freund; Robert E. Schapire (J. Comput. Syst. Sci., 1997)

Predicting Nearly As Well As the Best Pruning of a Decision Tree. - David P. Helmbold; Robert E. Schapire (Mach. Learn., 1997)

A Comparison of New and Old Algorithms for a Mixture Estimation Problem. - David P. Helmbold; Robert E. Schapire; Yoram Singer; Manfred K. Warmuth (Mach. Learn., 1997)

Boosting and Rocchio Applied to Text Filtering. - Robert E. Schapire; Yoram Singer; Amit Singhal 001 (SIGIR, 1998)

Theoretical Views of Boosting and Applications. - Robert E. Schapire (ALT, 1999)

Boosting Applied to Tagging and PP Attachment. - Steven Abney; Robert E. Schapire; Yoram Singer (EMNLP, 1999)

Theoretical Views of Boosting. - Robert E. Schapire (EuroCOLT, 1999)

A Brief Introduction to Boosting. - Robert E. Schapire (IJCAI, 1999)

Learning to Order Things. - William W. Cohen; Robert E. Schapire; Yoram Singer (J. Artif. Intell. Res., 1999)

Large Margin Classification Using the Perceptron Algorithm. - Yoav Freund; Robert E. Schapire (Mach. Learn., 1999)

Improved Boosting Algorithms Using Confidence-rated Predictions. - Robert E. Schapire; Yoram Singer (Mach. Learn., 1999)

Boosting for Document Routing. - Raj D. Iyer; David D. Lewis; Robert E. Schapire; Yoram Singer; Amit Singhal 001 (CIKM, 2000)

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

Gambling in a rigged casino: The adversarial multi-armed bandit problem - Peter Auer; Nicolò Cesa-Bianchi; Yoav Freund; Robert E. Schapire (Electron. Colloquium Comput. Complex., 2000)

Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. - Erin L. Allwein; Robert E. Schapire; Yoram Singer (J. Mach. Learn. Res., 2000)

BoosTexter: A Boosting-based System for Text Categorization. - Robert E. Schapire; Yoram Singer (Mach. Learn., 2000)

Why averaging classifiers can protect against overfitting. - Yoav Freund; Yishay Mansour; Robert E. Schapire (AISTATS, 2001)

A Generalization of Principal Components Analysis to the Exponential Family. - Michael Collins 001; Sanjoy Dasgupta; Robert E. Schapire (NIPS, 2001)

Drifting Games. - Robert E. Schapire (Mach. Learn., 2001)

ATTac-2001: A Learning, Autonomous Bidding Agent. - Peter Stone 001; Robert E. Schapire; János A. Csirik; Michael L. Littman; David A. McAllester (AMEC, 2002)

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 (ICASSP, 2002)

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

Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. - Robert E. Schapire; Peter Stone 001; David A. McAllester; Michael L. Littman; János A. Csirik (ICML, 2002)

AT&t help desk. - Giuseppe Di Fabbrizio; Dawn Dutton; Narendra K. Gupta; Barbara Hollister; Mazin G. Rahim; Giuseppe Riccardi; Robert E. Schapire; Juergen Schroeter (INTERSPEECH, 2002)

Advances in Boosting. - Robert E. Schapire (UAI, 2002)

Logistic Regression, AdaBoost and Bregman Distances. - Michael Collins 001; Robert E. Schapire; Yoram Singer (Mach. Learn., 2002)

The Nonstochastic Multiarmed Bandit Problem. - Peter Auer; Nicolò Cesa-Bianchi; Yoav Freund; Robert E. Schapire (SIAM J. Comput., 2002)

Active learning for spoken language understanding. - Gökhan Tür; Robert E. Schapire; Dilek Hakkani-Tür (ICASSP (1), 2003)

On the Dynamics of Boosting. - Cynthia Rudin; Ingrid Daubechies; Robert E. Schapire (NIPS, 2003)

Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. - Peter Stone 001; Robert E. Schapire; Michael L. Littman; János A. Csirik; David A. McAllester (J. Artif. Intell. Res., 2003)

An Efficient Boosting Algorithm for Combining Preferences. - Yoav Freund; Raj D. Iyer; Robert E. Schapire; Yoram Singer (J. Mach. Learn. Res., 2003)

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

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

A maximum entropy approach to species distribution modeling. - Steven J. Phillips; Miroslav Dudík; Robert E. Schapire (ICML, 2004)

The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. - Cynthia Rudin; Ingrid Daubechies; Robert E. Schapire (J. Mach. Learn. Res., 2004)

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

Correcting sample selection bias in maximum entropy density estimation. - Miroslav Dudík; Robert E. Schapire; Steven J. Phillips (NIPS, 2005)

Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. - Aurélie C. Lozano; Sanjeev R. Kulkarni; Robert E. Schapire (NIPS, 2005)

Efficient Multiclass Implementations of L1-Regularized Maximum Entropy - Patrick Haffner; Steven J. Phillips; Robert E. Schapire (CoRR, 2005)

Combining active and semi-supervised learning for spoken language understanding. - Gökhan Tür; Dilek Hakkani-Tür; Robert E. Schapire (Speech Commun., 2005)

Boosting with prior knowledge for call classification. - Robert E. Schapire; Marie Rochery; Mazin G. Rahim; Narendra K. Gupta (IEEE Trans. Speech Audio Process., 2005)

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

Algorithms for portfolio management based on the Newton method. - Amit Agarwal 008; Elad Hazan; Satyen Kale; Robert E. Schapire (ICML, 2006)

How boosting the margin can also boost classifier complexity. - Lev Reyzin; Robert E. Schapire (ICML, 2006)

Hierarchical multi-label prediction of gene function. - Zafer Barutçuoglu; Robert E. Schapire; Olga G. Troyanskaya (Bioinform., 2006)

Hierarchical maximum entropy density estimation. - Miroslav Dudík; David M. Blei; Robert E. Schapire (ICML, 2007)

FilterBoost: Regression and Classification on Large Datasets. - Joseph K. Bradley; Robert E. Schapire (NIPS, 2007)

A Game-Theoretic Approach to Apprenticeship Learning. - Umar Syed; Robert E. Schapire (NIPS, 2007)

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

Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling. - Miroslav Dudík; Steven J. Phillips; Robert E. Schapire (J. Mach. Learn. Res., 2007)

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

Apprenticeship learning using linear programming. - Umar Syed; Michael H. Bowling; Robert E. Schapire (ICML, 2008)

From Optimization to Regret Minimization and Back Again. - Ioannis C. Avramopoulos; Jennifer Rexford; Robert E. Schapire (SysML, 2008)

On reoptimizing multi-class classifiers. - Chris Bourke; Kun Deng; Stephen D. Scott 001; Robert E. Schapire; N. V. Vinodchandran (Mach. Learn., 2008)

Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields. - Zafer Barutçuoglu; Edoardo M. Airoldi; Vanessa Dumeaux; Robert E. Schapire; Olga G. Troyanskaya (Bioinform., 2009)

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost. - Cynthia Rudin; Robert E. Schapire (J. Mach. Learn. Res., 2009)

Speed and Sparsity of Regularized Boosting. - Yongxin Taylor Xi; Zhen James Xiang; Peter J. Ramadge; Robert E. Schapire (AISTATS, 2009)

The Convergence Rate of AdaBoost. - Robert E. Schapire (COLT, 2010)

Non-Stochastic Bandit Slate Problems. - Satyen Kale; Lev Reyzin; Robert E. Schapire (NIPS, 2010)

A Reduction from Apprenticeship Learning to Classification. - Umar Syed; Robert E. Schapire (NIPS, 2010)

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

A contextual-bandit approach to personalized news article recommendation. - Lihong Li 001; Wei Chu; John Langford 001; Robert E. Schapire (WWW, 2010)

An Optimal High Probability Algorithm for the Contextual Bandit Problem - Alina Beygelzimer; John Langford 001; Lihong Li 001; Lev Reyzin; Robert E. Schapire (CoRR, 2010)

Learning with continuous experts using drifting games. - Indraneel Mukherjee; Robert E. Schapire (Theor. Comput. Sci., 2010)

Compressive sensing meets game theory. - Sina Jafarpour; Robert E. Schapire; Volkan Cevher (ICASSP, 2011)

A game theoretic approach to expander-based compressive sensing. - Sina Jafarpour; Volkan Cevher; Robert E. Schapire (ISIT, 2011)

Contextual Bandit Algorithms with Supervised Learning Guarantees. - Alina Beygelzimer; John Langford 001; Lihong Li 001; Lev Reyzin; Robert E. Schapire (AISTATS, 2011)

Contextual Bandits with Linear Payoff Functions. - Wei Chu; Lihong Li 001; Lev Reyzin; Robert E. Schapire (AISTATS, 2011)

Contextual Bandit Learning with Predictable Rewards. - Alekh Agarwal; Miroslav Dudík; Satyen Kale; John Langford 001; Robert E. Schapire (AISTATS, 2012)

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

Explaining AdaBoost. - Robert E. Schapire (Empirical Inference, 2013)

Online Learning with Unknown Time Horizon. - Haipeng Luo; Robert E. Schapire (CoRR, 2013)

Advances in Boosting (Invited Talk) - Robert E. Schapire (CoRR, 2013)

The rate of convergence of AdaBoost. - Indraneel Mukherjee; Cynthia Rudin; Robert E. Schapire (J. Mach. Learn. Res., 2013)

A theory of multiclass boosting. - Indraneel Mukherjee; Robert E. Schapire (J. Mach. Learn. Res., 2013)

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

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

Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware. - Zhuo Wang 001; Robert E. Schapire; Naveen Verma (ICASSP, 2014)

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. - Alekh Agarwal; Daniel J. Hsu; Satyen Kale; John Langford 001; Lihong Li 001; Robert E. Schapire (ICML, 2014)

Towards Minimax Online Learning with Unknown Time Horizon. - Haipeng Luo; Robert E. Schapire (ICML, 2014)

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

Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations. - Aurélie C. Lozano; Sanjeev R. Kulkarni; Robert E. Schapire (IEEE Trans. Inf. Theory, 2014)

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

Learning and inference in the presence of corrupted inputs. - Uriel Feige; Yishay Mansour; Robert E. Schapire (COLT, 2015)

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

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

Efficient and Parsimonious Agnostic Active Learning. - Tzu-Kuo Huang; Alekh Agarwal; Daniel J. Hsu; John Langford 001; Robert E. Schapire (NIPS, 2015)

Fast Convergence of Regularized Learning in Games. - Vasilis Syrgkanis; Alekh Agarwal; Haipeng Luo; Robert E. Schapire (NIPS, 2015)

Achieving All with No Parameters: Adaptive NormalHedge. - Haipeng Luo; Robert E. Schapire (CoRR, 2015)

Convex Risk Minimization and Conditional Probability Estimation. - Matus Telgarsky; Miroslav Dudík; Robert E. Schapire (CoRR, 2015)

Functional Frank-Wolfe Boosting for General Loss Functions. - Chu Wang; Yingfei Wang; Weinan E; Robert E. Schapire (CoRR, 2015)

Error Adaptive Classifier Boosting (EACB): Leveraging Data-Driven Training Towards Hardware Resilience for Signal Inference. - Zhuo Wang 001; Robert E. Schapire; Naveen Verma (IEEE Trans. Circuits Syst. I Regul. Pap., 2015)

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

Efficient Algorithms for Adversarial Contextual Learning. - Vasilis Syrgkanis; Akshay Krishnamurthy; Robert E. Schapire (ICML, 2016)

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

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains. - David Abel; Alekh Agarwal; Fernando Diaz 001; Akshay Krishnamurthy; Robert E. Schapire (CoRR, 2016)

Multi-Source Domain Adaptation Using Approximate Label Matching. - Jordan T. Ash; Robert E. Schapire (CoRR, 2016)

Oracle-Efficient Learning and Auction Design. - Miroslav Dudík; Nika Haghtalab; Haipeng Luo; Robert E. Schapire; Vasilis Syrgkanis; Jennifer Wortman Vaughan (CoRR, 2016)

Open Problem: First-Order Regret Bounds for Contextual Bandits. - Alekh Agarwal; Akshay Krishnamurthy; John Langford 001; Haipeng Luo; Robert E. Schapire (COLT, 2017)

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

Contextual Decision Processes with low Bellman rank are PAC-Learnable. - Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (ICML, 2017)

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

Practical Contextual Bandits with Regression Oracles. - Dylan J. Foster; Alekh Agarwal; Miroslav Dudík; Haipeng Luo; Robert E. Schapire (ICML, 2018)

Learning Deep ResNet Blocks Sequentially using Boosting Theory. - Furong Huang; Jordan T. Ash; John Langford 001; Robert E. Schapire (ICML, 2018)

On Oracle-Efficient PAC RL with Rich Observations. - Christoph Dann; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (NeurIPS, 2018)

On Polynomial Time PAC Reinforcement Learning with Rich Observations. - Christoph Dann; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (CoRR, 2018)

Reinforcement Learning with Convex Constraints. - Sobhan Miryoosefi; Kianté Brantley; Hal Daumé III; Miroslav Dudík; Robert E. Schapire (NeurIPS, 2019)

Interactive Learning of a Dynamic Structure. - Ehsan Emamjomeh-Zadeh; David Kempe 001; Mohammad Mahdian; Robert E. Schapire (ALT, 2020)

Gradient descent follows the regularization path for general losses. - Ziwei Ji; Miroslav Dudík; Robert E. Schapire; Matus Telgarsky (COLT, 2020)

Oracle-efficient Online Learning and Auction Design. - Miroslav Dudík; Nika Haghtalab; Haipeng Luo; Robert E. Schapire; Vasilis Syrgkanis; Jennifer Wortman Vaughan (J. ACM, 2020)

Interactive Learning from Activity Description. - Khanh Nguyen; Dipendra Misra; Robert E. Schapire; Miroslav Dudík; Patrick Shafto (ICML, 2021)

Multiclass Boosting and the Cost of Weak Learning. - Nataly Brukhim; Elad Hazan; Shay Moran; Indraneel Mukherjee; 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; Miroslav Dudík; Robert E. Schapire (NeurIPS, 2021)

Contextual search in the presence of irrational agents. - Akshay Krishnamurthy; Thodoris Lykouris; Chara Podimata; Robert E. Schapire (STOC, 2021)

Provably sample-efficient RL with side information about latent dynamics. - Yao Liu; Dipendra Misra; Miro Dudík; Robert E. Schapire (NeurIPS, 2022)

Convex Analysis at Infinity: An Introduction to Astral Space. - Miroslav Dudík; Ziwei Ji; Robert E. Schapire; Matus Telgarsky (CoRR, 2022)

Adversarial Bandits with Knapsacks. - Nicole Immorlica; Karthik Abinav Sankararaman; Robert E. Schapire; Aleksandrs Slivkins (J. ACM, 2022)

A Unified Model and Dimension for Interactive Estimation. - Nataly Brukhim; Miro Dudík; Aldo Pacchiano; Robert E. Schapire (NeurIPS, 2023)

Contextual Search in the Presence of Adversarial Corruptions. - Akshay Krishnamurthy; Thodoris Lykouris; Chara Podimata; Robert E. Schapire (Oper. Res., 2023)

Lexicographic Optimization: Algorithms and Stability. - Jacob D. Abernethy; Robert E. Schapire; Umar Syed (AISTATS, 2024)

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

On the Hardness of Bandit Learning. - Nataly Brukhim; Aldo Pacchiano; Miroslav Dudík; Robert E. Schapire (COLT, 2025)