Better Fair Algorithms for Contextual Bandits ∗ Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth University of Pennsylvania

Source URL: fatml.mysociety.org

Fair Algorithms for Infinite Contextual Bandits ∗ Matthew Joseph, Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth University of Pennsylvania

Source URL: fatml.mysociety.org

A Convex Framework for Fair Regression∗ Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph Michael Kearns, Jamie Morgenstern, Seth Neel, Aaron Roth University of Pennsylvania Abstract

Source URL: fatml.mysociety.org

Algorithmic Trading and Computational Finance Michael Kearns Computer and Information Science University of Pennsylvania STOC Tutorial

Source URL: www.cis.upenn.edu

Marginals-to-Models Reducibility Michael Kearns University of Pennsylvania

Source URL: theory.stanford.edu

• Mathematics
• Computational complexity theory
• Graph theory
• Graphical models
• Combinatorial optimization
• Convex optimization
• Operations research
• Probability theory
• Polynomial
• Ellipsoid method
• Markov random field
• Bayesian network

http://priml.upenn.edu PRιML Penn Research in Machine Learning School of Engineering & Applied Science | The Wharton School

Source URL: www.seas.upenn.edu

• Michael Kearns
• Mohri
• Boosting
• Machine learning
• Bayesian network
• Robert Schapire
• Littmann
• Nonparametric statistics
• Ben Taskar

Privacy-Preserving Belief Propagation and Sampling Michael Kearns, Jinsong Tan, and Jennifer Wortman Department of Computer and Information Science University of Pennsylvania, Philadelphia, PA 19104

Source URL: www.jennwv.com

Learning from Multiple Sources Koby Crammer, Michael Kearns, Jennifer Wortman Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104

Source URL: www.jennwv.com

Learning from Collective Behavior Michael Kearns Computer and Information Science University of Pennsylvania

Source URL: www.jennwv.com