Robert E. Schapire

Orcid: 0000-0001-8616-1611

Affiliations:
  • Microsoft Research, Cambridge, MA, USA
  • Princeton University, USA (former)


According to our database1, Robert E. Schapire authored at least 157 papers between 1987 and 2023.

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Bibliography

2023
Contextual Search in the Presence of Adversarial Corruptions.
Oper. Res., July, 2023

A Unified Model and Dimension for Interactive Estimation.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Adversarial Bandits with Knapsacks.
J. ACM, 2022

Convex Analysis at Infinity: An Introduction to Astral Space.
CoRR, 2022

Provably sample-efficient RL with side information about latent dynamics.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021
Contextual search in the presence of irrational agents.
Proceedings of the STOC '21: 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2021

Bayesian decision-making under misspecified priors with applications to meta-learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Multiclass Boosting and the Cost of Weak Learning.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Interactive Learning from Activity Description.
Proceedings of the 38th International Conference on Machine Learning, 2021

2020
Oracle-efficient Online Learning and Auction Design.
J. ACM, 2020

Gradient descent follows the regularization path for general losses.
Proceedings of the Conference on Learning Theory, 2020

Interactive Learning of a Dynamic Structure.
Proceedings of the Algorithmic Learning Theory, 2020

2019
Reinforcement Learning with Convex Constraints.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018
On Polynomial Time PAC Reinforcement Learning with Rich Observations.
CoRR, 2018

On Oracle-Efficient PAC RL with Rich Observations.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Learning Deep ResNet Blocks Sequentially using Boosting Theory.
Proceedings of the 35th International Conference on Machine Learning, 2018

Practical Contextual Bandits with Regression Oracles.
Proceedings of the 35th International Conference on Machine Learning, 2018

Robust Inference for Multiclass Classification.
Proceedings of the Algorithmic Learning Theory, 2018

2017
Contextual Decision Processes with low Bellman rank are PAC-Learnable.
Proceedings of the 34th International Conference on Machine Learning, 2017

Corralling a Band of Bandit Algorithms.
Proceedings of the 30th Conference on Learning Theory, 2017

Open Problem: First-Order Regret Bounds for Contextual Bandits.
Proceedings of the 30th Conference on Learning Theory, 2017

2016
Oracle-Efficient Learning and Auction Design.
CoRR, 2016

Multi-Source Domain Adaptation Using Approximate Label Matching.
CoRR, 2016

Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains.
CoRR, 2016

Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Efficient Algorithms for Adversarial Contextual Learning.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Instance-dependent Regret Bounds for Dueling Bandits.
Proceedings of the 29th Conference on Learning Theory, 2016

2015
Error Adaptive Classifier Boosting (EACB): Leveraging Data-Driven Training Towards Hardware Resilience for Signal Inference.
IEEE Trans. Circuits Syst. I Regul. Pap., 2015

Functional Frank-Wolfe Boosting for General Loss Functions.
CoRR, 2015

Convex Risk Minimization and Conditional Probability Estimation.
CoRR, 2015

Achieving All with No Parameters: Adaptive NormalHedge.
CoRR, 2015

Fast Convergence of Regularized Learning in Games.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Efficient and Parsimonious Agnostic Active Learning.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Collaborative Place Models.
Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, 2015

Achieving All with No Parameters: AdaNormalHedge.
Proceedings of The 28th Conference on Learning Theory, 2015

Learning and inference in the presence of corrupted inputs.
Proceedings of The 28th Conference on Learning Theory, 2015

Contextual Dueling Bandits.
Proceedings of The 28th Conference on Learning Theory, 2015

2014
Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations.
IEEE Trans. Inf. Theory, 2014

A Drifting-Games Analysis for Online Learning and Applications to Boosting.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Towards Minimax Online Learning with Unknown Time Horizon.
Proceedings of the 31th International Conference on Machine Learning, 2014

Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits.
Proceedings of the 31th International Conference on Machine Learning, 2014

Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware.
Proceedings of the IEEE International Conference on Acoustics, 2014

Robust Multi-objective Learning with Mentor Feedback.
Proceedings of The 27th Conference on Learning Theory, 2014

Collaborative Ranking for Local Preferences.
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013
A theory of multiclass boosting.
J. Mach. Learn. Res., 2013

The rate of convergence of AdaBoost.
J. Mach. Learn. Res., 2013

Advances in Boosting (Invited Talk)
CoRR, 2013

Online Learning with Unknown Time Horizon.
CoRR, 2013

Explaining AdaBoost.
Proceedings of the Empirical Inference - Festschrift in Honor of Vladimir N. Vapnik, 2013

2012
Open Problem: Does AdaBoost Always Cycle?
Proceedings of the COLT 2012, 2012

Contextual Bandit Learning with Predictable Rewards.
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

2011
Contextual Bandits with Linear Payoff Functions.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Contextual Bandit Algorithms with Supervised Learning Guarantees.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

A game theoretic approach to expander-based compressive sensing.
Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

Compressive sensing meets game theory.
Proceedings of the IEEE International Conference on Acoustics, 2011

2010
Learning with continuous experts using drifting games.
Theor. Comput. Sci., 2010

An Optimal High Probability Algorithm for the Contextual Bandit Problem
CoRR, 2010

A contextual-bandit approach to personalized news article recommendation.
Proceedings of the 19th International Conference on World Wide Web, 2010

Combining Spatial and Telemetric Features for Learning Animal Movement Models.
Proceedings of the UAI 2010, 2010

A Reduction from Apprenticeship Learning to Classification.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Non-Stochastic Bandit Slate Problems.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

The Convergence Rate of AdaBoost.
Proceedings of the COLT 2010, 2010

2009
Speed and Sparsity of Regularized Boosting.
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost.
J. Mach. Learn. Res., 2009

Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields.
Bioinform., 2009

2008
On reoptimizing multi-class classifiers.
Mach. Learn., 2008

From Optimization to Regret Minimization and Back Again.
Proceedings of the Third Workshop on Tackling Computer Systems Problems with Machine Learning Techniques, 2008

Apprenticeship learning using linear programming.
Proceedings of the Machine Learning, 2008

2007
Maximum Entropy Correlated Equilibria.
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling.
J. Mach. Learn. Res., 2007

Imitation Learning with a Value-Based Prior.
Proceedings of the UAI 2007, 2007

A Game-Theoretic Approach to Apprenticeship Learning.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

FilterBoost: Regression and Classification on Large Datasets.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Hierarchical maximum entropy density estimation.
Proceedings of the Machine Learning, 2007

2006
Hierarchical multi-label prediction of gene function.
Bioinform., 2006

How boosting the margin can also boost classifier complexity.
Proceedings of the Machine Learning, 2006

Algorithms for portfolio management based on the Newton method.
Proceedings of the Machine Learning, 2006

Maximum Entropy Distribution Estimation with Generalized Regularization.
Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

2005
Boosting with prior knowledge for call classification.
IEEE Trans. Speech Audio Process., 2005

Combining active and semi-supervised learning for spoken language understanding.
Speech Commun., 2005

Efficient Multiclass Implementations of L1-Regularized Maximum Entropy
CoRR, 2005

Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Correcting sample selection bias in maximum entropy density estimation.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Margin-Based Ranking Meets Boosting in the Middle.
Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

2004
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins.
J. Mach. Learn. Res., 2004

A maximum entropy approach to species distribution modeling.
Proceedings of the Machine Learning, 2004

Boosting Based on a Smooth Margin.
Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

Performance Guarantees for Regularized Maximum Entropy Density Estimation.
Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

2003
An Efficient Boosting Algorithm for Combining Preferences.
J. Mach. Learn. Res., 2003

Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions.
J. Artif. Intell. Res., 2003

On the Dynamics of Boosting.
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Active learning for spoken language understanding.
Proceedings of the 2003 IEEE International Conference on Acoustics, 2003

2002
The Nonstochastic Multiarmed Bandit Problem.
SIAM J. Comput., 2002

Logistic Regression, AdaBoost and Bregman Distances.
Mach. Learn., 2002

Advances in Boosting.
Proceedings of the UAI '02, 2002

AT&t help desk.
Proceedings of the 7th International Conference on Spoken Language Processing, ICSLP2002, 2002

Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation.
Proceedings of the Machine Learning, 2002

Incorporating Prior Knowledge into Boosting.
Proceedings of the Machine Learning, 2002

Combining prior knowledge and boosting for call classification in spoken language dialogue.
Proceedings of the IEEE International Conference on Acoustics, 2002

ATTac-2001: A Learning, Autonomous Bidding Agent.
Proceedings of the Agent-Mediated Electronic Commerce IV, 2002

2001
Drifting Games.
Mach. Learn., 2001

A Generalization of Principal Components Analysis to the Exponential Family.
Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

Why averaging classifiers can protect against overfitting.
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

2000
BoosTexter: A Boosting-based System for Text Categorization.
Mach. Learn., 2000

Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers.
J. Mach. Learn. Res., 2000

Gambling in a rigged casino: The adversarial multi-armed bandit problem
Electron. Colloquium Comput. Complex., 2000

On the Convergence Rate of Good-Turing Estimators.
Proceedings of the Thirteenth Annual Conference on Computational Learning Theory (COLT 2000), June 28, 2000

Boosting for Document Routing.
Proceedings of the 2000 ACM CIKM International Conference on Information and Knowledge Management, 2000

1999
Improved Boosting Algorithms Using Confidence-rated Predictions.
Mach. Learn., 1999

Large Margin Classification Using the Perceptron Algorithm.
Mach. Learn., 1999

Learning to Order Things.
J. Artif. Intell. Res., 1999

A Brief Introduction to Boosting.
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999

Theoretical Views of Boosting.
Proceedings of the Computational Learning Theory, 4th European Conference, 1999

Boosting Applied to Tagging and PP Attachment.
Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999

Theoretical Views of Boosting and Applications.
Proceedings of the Algorithmic Learning Theory, 10th International Conference, 1999

1998
Boosting and Rocchio Applied to Text Filtering.
Proceedings of the SIGIR '98: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1998

1997
A Comparison of New and Old Algorithms for a Mixture Estimation Problem.
Mach. Learn., 1997

Predicting Nearly As Well As the Best Pruning of a Decision Tree.
Mach. Learn., 1997

A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting.
J. Comput. Syst. Sci., 1997

How to use expert advice.
J. ACM, 1997

Efficient Learning of Typical Finite Automata from Random Walks.
Inf. Comput., 1997

Using and Combining Predictors That Specialize.
Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing, 1997

Boosting the margin: A new explanation for the effectiveness of voting methods.
Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), 1997

Using output codes to boost multiclass learning problems.
Proceedings of the Fourteenth International Conference on Machine Learning (ICML 1997), 1997

1996
On the Worst-Case Analysis of Temporal-Difference Learning Algorithms.
Mach. Learn., 1996

Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples.
J. Comput. Syst. Sci., 1996

Training Algorithms for Linear Text Classifiers.
Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1996

On-Line Portfolio Selection Using Multiplicative Updates.
Proceedings of the Machine Learning, 1996

Experiments with a New Boosting Algorithm.
Proceedings of the Machine Learning, 1996

Game Theory, On-Line Prediction and Boosting.
Proceedings of the Ninth Annual Conference on Computational Learning Theory, 1996

1995
On the Sample Complexity of Weakly Learning
Inf. Comput., March, 1995

Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries.
Proceedings of the 36th Annual Symposium on Foundations of Computer Science, 1995

Gambling in a Rigged Casino: The Adversarial Multi-Arm Bandit Problem.
Proceedings of the 36th Annual Symposium on Foundations of Computer Science, 1995

1994
Learning Probabilistic Read-once Formulas on Product Distributions.
Mach. Learn., 1994

Toward Efficient Agnostic Learning.
Mach. Learn., 1994

Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension.
Mach. Learn., 1994

Efficient Distribution-Free Learning of Probabilistic Concepts.
J. Comput. Syst. Sci., 1994

Diversity-Based Inference of Finite Automata.
J. ACM, 1994

On the learnability of discrete distributions.
Proceedings of the Twenty-Sixth Annual ACM Symposium on Theory of Computing, 1994

1993
Inference of Finite Automata Using Homing Sequences
Inf. Comput., April, 1993

Learning Binary Relations and Total Orders.
SIAM J. Comput., 1993

Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions.
SIAM J. Comput., 1993

Boosting Performance in Neural Networks.
Int. J. Pattern Recognit. Artif. Intell., 1993

1992
Improving Performance in Neural Networks Using a Boosting Algorithm.
Proceedings of the Advances in Neural Information Processing Systems 5, [NIPS Conference, Denver, Colorado, USA, November 30, 1992

Design and analysis of efficient learning algorithms.
ACM Doctoral dissertation award ; 1991, MIT Press, ISBN: 978-0-262-19325-2, 1992

1991
Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods.
Proceedings of the Advances in Neural Information Processing Systems 4, 1991

1990
The Strength of Weak Learnability.
Mach. Learn., 1990

Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract)
Proceedings of the 31st Annual Symposium on Foundations of Computer Science, 1990

Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract)
Proceedings of the 31st Annual Symposium on Foundations of Computer Science, 1990

Pattern Languages are not Learnable.
Proceedings of the Third Annual Workshop on Computational Learning Theory, 1990

Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract).
Proceedings of the Third Annual Workshop on Computational Learning Theory, 1990

Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract).
Proceedings of the Third Annual Workshop on Computational Learning Theory, 1990

On the Sample Complexity of Weak Learning.
Proceedings of the Third Annual Workshop on Computational Learning Theory, 1990

1989
Inference of Finite Automata Using Homing Sequences (Extended Abstract)
Proceedings of the 21st Annual ACM Symposium on Theory of Computing, 1989

The Strength of Weak Learnability (Extended Abstract)
Proceedings of the 30th Annual Symposium on Foundations of Computer Science, Research Triangle Park, North Carolina, USA, 30 October, 1989

Learning Binary Relations and Total Orders (Extended Abstract)
Proceedings of the 30th Annual Symposium on Foundations of Computer Science, Research Triangle Park, North Carolina, USA, 30 October, 1989

1987
Diversity-Based Inference of Finite Automata (Extended Abstract)
Proceedings of the 28th Annual Symposium on Foundations of Computer Science, 1987


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