This monograph portrays optimization as a process. In many practical applications the
environment is so complex that it is infeasible to lay out a comprehensive theoretical model …

As a result of the new telecommunication ecosystem landscape, wireless communication
has become an interdisciplinary field whose future is shaped by several interacting …

We develop a learning principle and an efficient algorithm for batch learning from logged
bandit feedback. This learning setting is ubiquitous in online systems (eg, ad placement …

Most existing results about last-iterate convergence of learning dynamics are limited to two-
player zero-sum games, and only apply under rigid assumptions about what dynamics the …

Recently, Daskalakis, Fishelson, and Golowich (DFG)(NeurIPS '21) showed that if all agents
in a multi-player general-sum normal-form game employ Optimistic Multiplicative Weights …

In this paper, we consider a very general model for exploration-exploitation tradeoff which
allows arbitrary concave rewards and convex constraints on the decisions across time, in …

We introduce the online stochastic Convex Programming (CP) problem, a very general
version of stochastic online problems which allows arbitrary concave objectives and convex …

In standard reinforcement learning (RL), a learning agent seeks to optimize the overall
reward. However, many key aspects of a desired behavior are more naturally expressed as …

Blackwell approachability is a framework for reasoning about repeated games with vector-
valued payoffs. We introduce predictive Blackwell approachability, where an estimate of the …

In constrained reinforcement learning (RL), a learning agent seeks to not only optimize the
overall reward but also satisfy the additional safety, diversity, or budget constraints …