2026 Robert E. Schapire: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	90	589	572	315	305	224	148193

Research.com Recognitions

2016 - Member of the National Academy of Sciences
2014 - Member of the National Academy of Engineering For contributions to machine learning through invention and development of boosting algorithms.
2009 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to machine learning, including the theory and practice of boosting.
2004 - ACM Paris Kanellakis Theory and Practice Award Theory and practice of boosting

Overview

Robert E. Schapire is affiliated with Microsoft in the United States. Their research spans several areas in computer science and decision sciences, with a focus on artificial intelligence and operations research. Schapire has produced scholarly work primarily published in venues such as arXiv (Cornell University), the Journal of the ACM, and Operations Research.

Their recent publications include:

Adversarial Bandits with Knapsacks, 2022, Journal of the ACM
Bayesian decision-making under misspecified priors with applications to meta-learning, 2021, arXiv (Cornell University)
Gradient descent follows the regularization path for general losses, 2020, arXiv (Cornell University)
Interactive Learning from Activity Description, 2021, arXiv (Cornell University)
Contextual Search in the Presence of Adversarial Corruptions, 2022, Operations Research

Frequent co-authors in their collaborative works include:

Miroslav Dudík
Akshay Krishnamurthy
Thodoris Lykouris
Dipendra Misra
Aldo Pacchiano

The main fields of study for Schapire's research are:

Computer Science
Decision Sciences

Within these fields, they have contributed to various subfields including:

Artificial Intelligence
Management Science and Operations Research
Computer Networks and Communications
Electrical and Electronic Engineering
Computer Vision and Pattern Recognition

The primary topics covered in their work include:

Advanced Bandit Algorithms Research
Machine Learning and Algorithms
Reinforcement Learning in Robotics
Data Stream Mining Techniques
Auction Theory and Applications
Optimization and Search Problems
Adversarial Robustness in Machine Learning

The researcher has been recognized with multiple awards, such as:

Member of the National Academy of Sciences, 2016
Member of the National Academy of Engineering, 2014, for contributions to machine learning through invention and development of boosting algorithms
Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), 2009, for significant contributions to machine learning, including the theory and practice of boosting
ACM Paris Kanellakis Theory and Practice Award, 2004, for theory and practice of boosting

Best Publications

A Decision Theoretic Generalization of On-Line Learning and an Application to Boosting

Y. Freund;R. Schapire

27833
Citations
Experiments with a new boosting algorithm

Yoav Freund;Robert E. Schapire

11676
Citations
The Strength of Weak Learnability

Robert E. Schapire

6489
Citations
A Short Introduction to Boosting

Yoav Freund;Robert E. Schapire

4775
Citations
Improved boosting algorithms using confidence-rated predictions

Robert E. Schapire;Yoram Singer

4580
Citations
Boosting the margin: a new explanation for the effectiveness of voting methods

Robert E. Schapire;Yoav Freund;Peter Bartlett;Wee Sun Lee

3849
Citations
BoosTexter: A Boosting-based Systemfor Text Categorization

Robert E. Schapire;Yoram Singer

3226
Citations
A maximum entropy approach to species distribution modeling

Steven J. Phillips;Miroslav Dudík;Robert E. Schapire

3116
Citations
An efficient boosting algorithm for combining preferences

Yoav Freund;Raj Iyer;Robert E. Schapire;Yoram Singer

3027
Citations
The Boosting Approach to Machine Learning An Overview

Robert E. Schapire

2921
Citations
A contextual-bandit approach to personalized news article recommendation

Lihong Li;Wei Chu;John Langford;Robert E. Schapire

2880
Citations
Opening the black box: an open-source release of Maxent

Steven J. Phillips;Robert P. Anderson;Robert P. Anderson;Miroslav Dudík;Robert E. Schapire

2754
Citations
Reducing multiclass to binary: a unifying approach for margin classifiers

Erin L. Allwein;Robert E. Schapire;Yoram Singer

2753
Citations
The Nonstochastic Multiarmed Bandit Problem

Peter Auer;Nicolò Cesa-Bianchi;Yoav Freund;Robert E. Schapire

2747
Citations
A brief introduction to boosting

Robert E. Schapire

1781
Citations
Large margin classification using the perceptron algorithm

Yoav Freund;Robert E. Schapire

1666
Citations
Explaining AdaBoost

Unknown

1415
Citations
Boosting: Foundations and Algorithms

Robert E. Schapire;Yoav Freund

1043
Citations
Gambling in a rigged casino: The adversarial multi-armed bandit problem

P. Auer;N. Cesa-Bianchi;Y. Freund;R. Schapire

1037
Citations
Contextual bandits with linear Payoff functions

Wei Chu;Lihong Li;Lev Reyzin;Robert E. Schapire

815
Citations
Strength of weak learnability

Robert E. Schapire

775
Citations
Boosting the margin: A new explanation for the effectiveness of voting methods

Robert E. Schapire;Yoav Freund;Peter Barlett;Wee Sun Lee

711
Citations
Foundations of Machine Learning

Robert E. Schapire;Yoav Freund

261
Citations

Frequent Co-Authors

Yoav Freund University of California, San Diego

Miroslav Dudík Microsoft (United States)

Alekh Agarwal Google (United States)

Michael Kearns University of Pennsylvania

John Langford Microsoft (United States)

Yoram Singer Princeton University

Sally A. Goldman Google

Ronald L. Rivest MIT

Cynthia Rudin Duke University

Akshay Krishnamurthy Microsoft (United States)

External Links

Wikipedia of Robert E. Schapire Personal website of Robert E. Schapire Google Scholar page

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