Michael Kearns

发表

Behavioral Conflict and Fairness in Social Networks

Yevgeniy Vorobeychik, J. Stephen Judd, Michael Kearns, 2011, WINE.

Umesh V. Vazirani, Michael Kearns, M. Kearns, 1995, SIGACT News.

Efficient distribution-free learning of probabilistic concepts

Robert E. Schapire, Michael Kearns, R. Schapire, 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

Near-Optimal Reinforcement Learning in Polynomial Time

Michael Kearns, Satinder P. Singh, Satinder Singh, 2002, Machine Learning.

An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering

Yishay Mansour, Andrew Y. Ng, Michael Kearns, 1997, UAI.

On the Boosting Ability of Top-Down Decision Tree Learning Algorithms

Yishay Mansour, Michael Kearns, Y. Mansour, 1999, J. Comput. Syst. Sci..

Approximate Planning in Large POMDPs via Reusable Trajectories

Yishay Mansour, Andrew Y. Ng, Michael Kearns, 1999, NIPS.

Efficient distribution-free learning of probabilistic concepts

Robert E. Schapire, Michael Kearns, R. Schapire, 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

Online Learning and Profit Maximization from Revealed Preferences

Aaron Roth, Michael Kearns, Kareem Amin, 2014, AAAI.

Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods

David Haussler, Manfred Opper, Robert E. Schapire, 1991, NIPS.

Experiments in social computation: (and the data they generate)

Michael Kearns, 2012, KDD.

On the Sample Complexity of Weakly Learning

Robert E. Schapire, Michael Kearns, Sally A. Goldman, 1995, Inf. Comput..

Testing Problems with Sublearning Sample Complexity

Dana Ron, Michael Kearns, M. Kearns, 2000, J. Comput. Syst. Sci..

Private algorithms for the protected in social network search

Zhiwei Steven Wu, Grigory Yaroslavtsev, Michael Kearns, 2016, Proceedings of the National Academy of Sciences.

Toward efficient agnostic learning

Linda Sellie, Robert E. Schapire, Michael Kearns, 1992, COLT '92.

Trading in Markovian Price Models

Sham M. Kakade, Michael Kearns, S. Kakade, 2005, COLT.

An Empirical Study of Rich Subgroup Fairness for Machine Learning

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2018, FAT.

On the boosting ability of top-down decision tree learning algorithms

Yishay Mansour, Michael Kearns, Y. Mansour, 1996, STOC '96.

On the sample complexity of weak learning

Robert E. Schapire, Michael Kearns, Sally A. Goldman, 1990, COLT '90.

Computational Game Theory and AI

Michael Kearns, 2001, KI/ÖGAI.

Large-Scale Bandit Problems and KWIK Learning

Michael Kearns, Moez Draief, Kareem Amin, 2013, ICML.

Eliciting and Enforcing Subjective Individual Fairness pdf

Christopher Jung, Seth Neel, Aaron Roth, 2019, ArXiv.

Cobot: A Social Reinforcement Learning Agent

Peter Stone, Michael Kearns, Christian R. Shelton, 2001, NIPS.

CobotDS: a spoken dialogue system for chat

Michael Kearns, Diane J. Litman, Satinder P. Singh, 2002, AAAI/IAAI.

Learning in the presence of malicious errors

Ming Li, Michael Kearns, M. Kearns, 1993, STOC '88.

Depth-Workload Tradeoffs for Workforce Organization

Michael Kearns, Hoda Heidari, M. Kearns, 2013, HCOMP.

Private and Third-Party Randomization in Risk-Sensitive Equilibrium Concepts

Umar Syed, Michael Kearns, Mickey Brautbar, 2010, AAAI.

A polynomial-time algorithm for learning k-variable pattern languages from examples

Leonard Pitt, Michael Kearns, M. Kearns, 1989, COLT '89.

Learning from a Population of Hypotheses

H. Sebastian Seung, Michael Kearns, M. Kearns, 1993, COLT '93.

Fairness in Reinforcement Learning

Aaron Roth, Michael Kearns, Shahin Jabbari, 2016, ICML.

Fairness Incentives for Myopic Agents

Sampath Kannan, Aaron Roth, Zhiwei Steven Wu, 2017, EC.

Privacy and Truthful Equilibrium Selection for Aggregative Games

Aaron Roth, Zhiwei Steven Wu, Michael Kearns, 2014, WINE.

New Models for Competitive Contagion

Michael Kearns, Moez Draief, Hoda Heidari, 2014, AAAI.

Marginals-to-Models Reducibility

Tim Roughgarden, Michael Kearns, T. Roughgarden, 2013, NIPS.

Efficient Reinforcement Learning in Factored MDPs

Michael Kearns, Daphne Koller, M. Kearns, 1999, IJCAI.

Graphical Models for Bandit Problems pdf

Umar Syed, Michael Kearns, Kareem Amin, 2011, UAI.

Biased Voting and the Democratic Primary Problem

Michael Kearns, Jinsong Tan, J. Tan, 2008, WINE.

Equilibrium Characterization for Data Acquisition Games pdf

Michael Kearns, Shahin Jabbari, Zachary Schutzman, 2019, IJCAI.

Exact identification of circuits using fixed points of amplification functions

Robert E. Schapire, Michael Kearns, Sally A. Goldman, 1990, Proceedings [1990] 31st Annual Symposium on Foundations of Computer Science.

Learning from Collective Behavior

Michael Kearns, Jennifer Wortman Vaughan, M. Kearns, 2008, COLT.

Oblivious PAC Learning of Concept Hierarchies

Michael Kearns, M. Kearns, 1992, AAAI.

Market making and mean reversion

Tanmoy Chakraborty, Michael Kearns, M. Kearns, 2011, EC '11.

Testing problems with sub-learning sample complexity

Dana Ron, Michael Kearns, M. Kearns, 1998, COLT' 98.

Cryptographic Primitives Based on Hard Learning Problems

Richard J. Lipton, Avrim Blum, Michael Kearns, 1993, CRYPTO.

Automatic Optimization of Dialogue Management

Marilyn A. Walker, Michael Kearns, Diane J. Litman, 2000, COLING.

Colonel Blotto on Facebook: the effect of social relations on strategic interaction

Pushmeet Kohli, Michael Kearns, Thore Graepel, 2012, WebSci '12.

Strategic Network Formation with Attack and Immunization

Sanjeev Khanna, Michael Kearns, Sanjeev Goyal, 2016, WINE.

Nash Convergence of Gradient Dynamics in Iterated General-Sum Games pdf

Yishay Mansour, Michael Kearns, Satinder P. Singh, 2013, ArXiv.

Learning in the Presence of Malicious Errors

Ming Li, Michael Kearns, 1993, SIAM J. Comput..

Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions

Robert E. Schapire, Michael Kearns, Sally A. Goldman, 1993, SIAM J. Comput..

Inference in Multilayer Networks via Large Deviation Bounds

Lawrence K. Saul, Michael Kearns, M. Kearns, 1998, NIPS.

On the complexity of teaching

Michael Kearns, Sally A. Goldman, M. Kearns, 1991, COLT '91.

Weakly learning DNF and characterizing statistical query learning using Fourier analysis

Yishay Mansour, Avrim Blum, Michael Kearns, 1994, STOC '94.

Fair Algorithms for Machine Learning

Michael Kearns, M. Kearns, 2017, EC.

A Convex Framework for Fair Regression pdf

Seth Neel, Aaron Roth, Michael Kearns, 2017, ArXiv.

Bandits, Query Learning, and the Haystack Dimension

Umar Syed, Michael Kearns, Kareem Amin, 2011, COLT.

John Langford, Luis E. Ortiz, Sham M. Kakade, 2003, EC '03.

An Efficient, Exact Algorithm for Solving Tree-Structured Graphical Games

Michael L. Littman, Michael Kearns, Satinder P. Singh, 2001, NIPS.

Efficient algorithms for learning to play repeated games against computationally bounded adversaries

Ronitt Rubinfeld, Dana Ron, Yishay Mansour, 1995, Proceedings of IEEE 36th Annual Foundations of Computer Science.

A Small World Threshold for Economic Network Formation

Michael Kearns, Eyal Even-Dar, Eyal Even-Dar, 2006, NIPS.

Algorithmic Stability and Sanity-Check Bounds for Leave-One-Out Cross-Validation

Dana Ron, Michael Kearns, M. Kearns, 1997, Neural Computation.

Applying the Waek Learning Framework to Understand and Improve C4.5

Thomas G. Dietterich, Yishay Mansour, Michael Kearns, 1996, ICML.

Finite-Sample Convergence Rates for Q-Learning and Indirect Algorithms

Michael Kearns, Satinder P. Singh, Satinder Singh, 1998, NIPS.

Tight Policy Regret Bounds for Improving and Decaying Bandits

Aaron Roth, Michael Kearns, Hoda Heidari, 2016, IJCAI.

Fast Planning in Stochastic Games

Yishay Mansour, Michael Kearns, Satinder P. Singh, 2000, UAI.

Local Algorithms for Finding Interesting Individuals in Large Networks

Michael Kearns, Mickey Brautbar, M. Kearns, 2010, ICS.

Automatic Detection of Poor Speech Recognition at the Dialogue Level

Marilyn A. Walker, Michael Kearns, Diane J. Litman, 1999, ACL.

Fair Learning in Markovian Environments pdf

Aaron Roth, Michael Kearns, Shahin Jabbari, 2016, ArXiv.

Behavioral experiments on a network formation game

Yevgeniy Vorobeychik, J. Stephen Judd, Michael Kearns, 2011, EC '12.

Equivalence of models for polynomial learnability

David Haussler, Manfred K. Warmuth, Michael Kearns, 1988, COLT '88.

Cryptographic limitations on learning Boolean formulae and finite automata

Leslie G. Valiant, Michael Kearns, M. Kearns, 1994, JACM.

Efficient Learning of Typical Finite Automata from Random Walks

Ronitt Rubinfeld, Dana Ron, Linda Sellie, 1997, Inf. Comput..

Algorithms for Interdependent Security Games

Luis E. Ortiz, Michael Kearns, M. Kearns, 2003, NIPS.

Competitive contagion in networks

Michael Kearns, Sanjeev Goyal, S. Goyal, 2011, STOC '12.

Exploration in Metric State Spaces

John Langford, Sham M. Kakade, Michael Kearns, 2003, ICML.

Experiments in social computation

Michael Kearns, Michael Kearns, 2012, KDD.

Learning from a population of hypotheses

H. Sebastian Seung, Michael Kearns, 1995, Machine Learning.

(In)Stability properties of limit order dynamics

Yishay Mansour, Sham M. Kakade, Michael Kearns, 2006, EC '06.

Learning from Data of Variable Quality

Koby Crammer, Michael Kearns, Jennifer Wortman Vaughan, 2005, NIPS.

Learning and predicting dynamic networked behavior with graphical multiagent models

Michael P. Wellman, Michael Kearns, Quang Duong, 2012, AAMAS.

Nash Propagation for Loopy Graphical Games

Luis E. Ortiz, Michael Kearns, M. Kearns, 2002, NIPS.

Online Learning with an Unknown Fairness Metric

Christopher Jung, Aaron Roth, Michael Kearns, 2018, NeurIPS.

Efficient Inference for Complex Queries on Complex Distributions

Lirong Xia, Michael Kearns, Lili Dworkin, 2014, AISTATS.

Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension

David Haussler, Robert E. Schapire, Michael Kearns, 1991, COLT '91.

Algorithmic Stability and Sanity-Check Bounds for Leave-one-Out Cross-Validation

Dana Ron, Michael Kearns, M. Kearns, 1997, COLT.

Behavioral experiments on biased voting in networks

J. Stephen Judd, Michael Kearns, Jennifer Wortman Vaughan, 2009, Proceedings of the National Academy of Sciences.

Fair Algorithms for Learning in Allocation Problems

Christopher Jung, Seth Neel, Aaron Roth, 2018, FAT.

Predicting with Distributions pdf

Zhiwei Steven Wu, Michael Kearns, Michael Kearns, 2016, COLT.

On the learnability of discrete distributions

Ronitt Rubinfeld, Dana Ron, Linda Sellie, 1994, STOC '94.

A Computational Study of Feasible Repackings in the FCC Incentive Auctions

Michael Kearns, Lili Dworkin, 2014, ArXiv.

Electronic trading in order-driven markets: efficient execution

Katia P. Sycara, Michael Kearns, Yuriy Nevmyvaka, 2005, Seventh IEEE International Conference on E-Commerce Technology (CEC'05).

An Introduction to Computational Learning Theory

Umesh V. Vazirani, Michael Kearns, M. Kearns, 1994 .

Mathematical foundations for social computing

Tim Roughgarden, Michael Kearns, Arpita Ghosh, 2016, Commun. ACM.

A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split

Michael Kearns, M. Kearns, 1995, Neural Computation.

A Note on the Representational Incompatibility of Function Approximation and Factored Dynamics

Alexander Russell, Eric Allender, Cristopher Moore, 2002, NIPS.

Robust Mediators in Large Games pdf

Aaron Roth, Michael Kearns, Jonathan Ullman, 2015, ArXiv.

Learning from Multiple Sources

Koby Crammer, Michael Kearns, Jennifer Wortman Vaughan, 2006, NIPS.

Learning from Contagion (Without Timestamps)

Michael Kearns, Kareem Amin, Hoda Heidari, 2014, ICML.

Efficient noise-tolerant learning from statistical queries

Michael Kearns, M. Kearns, 1993, STOC.

Cryptographic Limitations on Learning Boolean Formulae and Finite Automata

Leslie G. Valiant, Michael Kearns, M. Kearns, 1993, Machine Learning: From Theory to Applications.

Structured interaction in game theory

Michael Kearns, 2003, TARK '03.

Fairness in Learning: Classic and Contextual Bandits

Aaron Roth, Michael Kearns, Jamie H. Morgenstern, 2016, NIPS.

Predicting Networked Strategic Behavior via Machine Learning and Game Theory

Michael Kearns, 2015 .

Bargaining Solutions in a Social Network

Tanmoy Chakraborty, Michael Kearns, T. Chakraborty, 2008, WINE.

Networks preserving evolutionary equilibria and the power of randomization

Michael Kearns, Siddharth Suri, Siddharth Suri, 2006, EC '06.

Rawlsian Fairness for Machine Learning pdf

Seth Neel, Aaron Roth, Michael Kearns, 2016, ArXiv.

Graphical Models for Game Theory

Michael L. Littman, Michael Kearns, Satinder P. Singh, 2001, UAI.

A general lower bound on the number of examples needed for learning

Leslie G. Valiant, David Haussler, Andrzej Ehrenfeucht, 1988, COLT '88.

Mechanism design in large games: incentives and privacy

Aaron Roth, Michael Kearns, Jonathan Ullman, 2012, ITCS.

Rigorous Learning Curve Bounds from Statistical Mechanics

David Haussler, H. Sebastian Seung, Naftali Tishby, 1994, COLT.

Privacy for the Protected (Only) pdf

Aaron Roth, Zhiwei Steven Wu, Grigory Yaroslavtsev, 2015, ArXiv.

Boosting Theory Towards Practice: Recent Developments in Decision Tree Induction and the Weak Learning Framework

Michael Kearns, M. Kearns, 1996, AAAI/IAAI, Vol. 2.

Network Formation under Random Attack and Probabilistic Spread pdf

Sanjeev Khanna, Michael Kearns, Shahin Jabbari, 2019, IJCAI.

Reasoning With Characteristic Models

Bart Selman, Henry A. Kautz, Michael Kearns, 1993, AAAI.

Cobot in LambdaMOO: An Adaptive Social Statistics Agent

Peter Stone, Michael Kearns, Christian R. Shelton, 2006, Autonomous Agents and Multi-Agent Systems.

Learning Boolean formulas

Leslie G. Valiant, Ming Li, Michael Kearns, 1994, JACM.

Budget Optimization for Sponsored Search: Censored Learning in MDPs pdf

Anton Schwaighofer, Michael Kearns, Kareem Amin, 2012, UAI.

Privacy-Preserving Belief Propagation and Sampling

Michael Kearns, Jennifer Wortman Vaughan, Jinsong Tan, 2007, NIPS.

Meritocratic Fairness for Cross-Population Selection

Aaron Roth, Zhiwei Steven Wu, Michael Kearns, 2017, ICML.

Regret to the Best vs. Regret to the Average

Yishay Mansour, Michael Kearns, Jennifer Wortman Vaughan, 2007, COLT.

Efficient distribution-free learning of probabilistic concepts (abstract)

Robert E. Schapire, Michael Kearns, 1990, COLT '90.

Large Deviation Methods for Approximate Probabilistic Inference pdf

Lawrence K. Saul, Michael Kearns, M. Kearns, 1998, UAI.

The Penn-Lehman Automated Trading Project

Luis E. Ortiz, Michael Kearns, 2003, IEEE Intell. Syst..

Bias-Variance Error Bounds for Temporal Difference Updates

Michael Kearns, Satinder P. Singh, Satinder Singh, 2000, COLT.

Competitive algorithms for VWAP and limit order trading

Yishay Mansour, Luis E. Ortiz, Sham M. Kakade, 2004, EC '04.

From "In" to "Over": Behavioral Experiments on Whole-Network Computation

Michael Kearns, Lili Dworkin, Lili Dworkin, 2015, HCOMP.

A social reinforcement learning agent

Peter Stone, Michael Kearns, Christian R. Shelton, 2001, AGENTS '01.

A Boosting Approach to Topic Spotting on Subdialogues

Marilyn A. Walker, Michael Kearns, Satinder P. Singh, 2000, ICML.

A Fast, Bottom-Up Decision Tree Pruning Algorithm with Near-Optimal Generalization

Yishay Mansour, Michael Kearns, Y. Mansour, 1998, ICML.

Exact Inference of Hidden Structure from Sample Data in noisy-OR Networks pdf

Yishay Mansour, Michael Kearns, 1998, UAI.

A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes

Yishay Mansour, Andrew Y. Ng, Michael Kearns, 1999, Machine Learning.

Efficient Nash Computation in Large Population Games with Bounded Influence pdf

Yishay Mansour, Michael Kearns, Y. Mansour, 2002, UAI.

Efficient learning of typical finite automata from random walks

Ronitt Rubinfeld, Dana Ron, Linda Sellie, 1993, STOC.

Behavioral experiments in networked trade

J. Stephen Judd, Michael Kearns, J. S. Judd, 2008, EC '08.

A behavioral study of bargaining in social networks

Tanmoy Chakraborty, J. Stephen Judd, Michael Kearns, 2010, EC '10.

Empirical Price Modeling for Sponsored Search

Alex Kulesza, Qian Liu, Ryan Gabbard, 2007, WINE.

Pursuit-Evasion Without Regret, with an Application to Trading

Michael Kearns, Lili Dworkin, Yuriy Nevmyvaka, 2014, ICML.

On the learnability of Boolean formulae

Leslie G. Valiant, Leonard Pitt, Ming Li, 1987, STOC.

Nash Convergence of Gradient Dynamics in General-Sum Games

Yishay Mansour, Michael Kearns, Satinder P. Singh, 2000, UAI.

Graphical Economics

Luis E. Ortiz, Sham M. Kakade, Michael Kearns, 2004, COLT.

Reinforcement Learning for Spoken Dialogue Systems

Marilyn A. Walker, Michael Kearns, Diane J. Litman, 1999, NIPS.

Cobot in LambdaMOO: A Social Statistics Agent

Peter Stone, Michael Kearns, Satinder P. Singh, 2000, AAAI/IAAI.

Meritocratic Fairness for Infinite and Contextual Bandits

Seth Neel, Aaron Roth, Michael Kearns, 2018, AIES.

Network bargaining: algorithms and structural results

Sanjeev Khanna, Tanmoy Chakraborty, Michael Kearns, 2009, EC '09.

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2017, ICML.

Regret to the best vs. regret to the average

Yishay Mansour, Michael Kearns, Jennifer Wortman Vaughan, 2007, Machine Learning.

Economic Properties of Social Networks

Luis E. Ortiz, Sham M. Kakade, Robin Pemantle, 2004, NIPS.

An Experimental and Theoretical Comparison of Model Selection Methods

Dana Ron, Yishay Mansour, Andrew Y. Ng, 1995, COLT '95.

Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System

Marilyn A. Walker, Michael Kearns, Diane J. Litman, 2000, AAAI/IAAI.

Horn Approximations of Empirical Data

Bart Selman, Henry A. Kautz, Michael Kearns, 1995, Artif. Intell..

Computational complexity of machine learning

Michael Kearns, M. Kearns, 1990, ACM distinguished dissertations.

Competitive contagion in networks

Michael Kearns, Sanjeev Goyal, Hoda Heidari, 2019, Games Econ. Behav..

A Clustering Coefficient Network Formation Game

Michael Kearns, Mickey Brautbar, Mickey Brautbar, 2010, SAGT.

Fair Algorithms for Infinite and Contextual Bandits

Seth Neel, Aaron Roth, Michael Kearns, 2016, 1610.09559.

An experimental and theoretical comparison of model selection methods

Dana Ron, Yishay Mansour, Andrew Y. Ng, 1995, COLT '95.

Reinforcement learning for optimized trade execution

Michael Kearns, Yi Feng, Yuriy Nevmyvaka, 2006, ICML.

An Algorithmic Framework for Fairness Elicitation

Seth Neel, Aaron Roth, Zhiwei Steven Wu, 2020, FORC.

Multiaccurate Proxies for Downstream Fairness pdf

Aaron Roth, Michael Kearns, Krishnaram Kenthapadi, 2021, FAccT.

Differentially Private Query Release Through Adaptive Projection pdf

Aaron Roth, Michael Kearns, Krishnaram Kenthapadi, 2021, ICML.

Beyond the Frontier: Fairness Without Accuracy Loss

Aaron Roth, Michael Kearns, Ira Globus-Harris, 2022 .

Lexicographically Fair Learning: Algorithms and Generalization

Aaron Roth, Michael Kearns, Saeed Sharifi-Malvajerdi, 2021, FORC.

Fair Algorithms for Infinite Contextual Bandits ∗

Seth Neel, Aaron Roth, Michael Kearns, 2017 .

Empirical Limitations on High-Frequency Trading Profitability

Alex Kulesza, Michael Kearns, Yuriy Nevmyvaka, 2010, The Journal of Trading.

Better Fair Algorithms for Contextual Bandits ∗

Seth Neel, Aaron Roth, Michael Kearns, 2017 .

Economics , Computer Science , and Policy Cross-fertilization of ideas and techniques between economics and computer science is yielding fresh insights that can help inform policy decisions

Michael Kearns, .

Empirical Limitations on High Frequency Trading Profitability

Alex Kulesza, Michael Kearns, Yuriy Nevmyvaka, 2010, 1007.2593.

Algorithmic Game Theory: Graphical Games

Michael Kearns, M. Kearns, Michael Kearns, 2007 .