In this paper, we provide the first efficient batched algorithm for contextual linear bandits with
large action spaces. Unlike existing batched algorithms that rely on action elimination, which …

In this paper, we address the stochastic contextual linear bandit problem, where a decision
maker is provided a context (a random set of actions drawn from a distribution). The …

We formalize the problem of contextual optimization through the lens of Bayesian
experimental design and propose CO-BED—a general, model-agnostic framework for …

Sequential decision‐making involves making informed decisions based on continuous
interactions with a complex environment. This process is ubiquitous in various applications …

We study a contextual bandit setting where the agent has access to causal side information,
in addition to the ability to perform multiple targeted experiments corresponding to …

In this work, we present and study a new framework for online learning in systems with
multiple users that provide user anonymity. Specifically, we extend the notion of bandits to …

We introduce the E $^ 4$ algorithm for the batched linear bandit problem, incorporating an
Explore-Estimate-Eliminate-Exploit framework. With a proper choice of exploration rate, we …

The growing availability of data and rapid advancements in machine learning are
revolutionizing decision-making. Often, these data come from distributed devices with low …