In this work, we introduce graphical modelsfor multi-player game theory, and give powerful
algorithms for computing their Nash equilibria in certain cases. An n-player game is given by …

We present the first large-scale empirical application of reinforcement learning to the important
problem of optimized trade execution in modern financial markets. Our experiments are …

We present new algorithms for reinforcement learning and prove that they have polynomial
bounds on the resources required to achieve near-optimal return in general Markov decision …

A critical issue for the application of Markov decision processes (MDPs) to realistic
problems is how the complexity of planning scales with the size of the MDP. In stochastic …

We study a practical extension to the Valiant model of machine learning from examples [v84]:
the presence of errors, possibly maliciously generated by an adversary, in the sample data. …

Objectives: Discussions of fairness in criminal justice risk assessments typically lack conceptual
precision. Rhetoric too often substitutes for careful analysis. In this article, we seek to …

In this paper, we prove the intractability of learning several classes of Boolean functions in
the distribution-free model (also called the Probably Approximately Correct or PAC model) of …

Addressing the ongoing examination of high-frequency trading practices in financial markets,
we report the results of an extensive empirical study estimating the maximum possible …

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani …
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a …

In this paper, we study the problem of learning in the presence of classification noise in the
probabilistic learning model of Valiant and its variants. In order to identify the class of “robust” …