While much progress has been made in understanding the minimax sample complexity of
reinforcement learning (RL)---the complexity of learning on the worst-case''instance---such …

In many applications, eg in healthcare and e-commerce, the goal of a contextual bandit may
be to learn an optimal treatment assignment policy at the end of the experiment. That is, to …

We study the problem of experiment planning with function approximation in contextual
bandit problems. In settings where there is a significant overhead to deploying adaptive …

Most linear experimental design problems assume homogeneous variance, while the
presence of heteroskedastic noise is present in many realistic settings. Let a learner have …

In an attempt to make algorithms fair, the machine learning literature has largely focused on
equalizing decisions, outcomes, or error rates across race or gender groups. To illustrate …

In digital marketing, experimenting with new website content is one of the key levers to
improve customer engagement. However, creating successful marketing content is a manual …

In dynamic discrete choice models, a commonly studied problem is estimating parameters of
agent reward functions (also known as' structural'parameters) using agent behavioral data …

Given a set of arms $\mathcal {Z}\subset\mathbb {R}^ d $ and an unknown parameter vector
$\theta_\ast\in\mathbb {R}^ d $, the pure exploration linear bandits problem aims to return …

Cache management is critical for Content Delivery Networks (CDNs), impacting their
performance and operational costs. Most production CDNs apply static, hand-tuned caching …

Problem definition: Personalized medicine (PM) seeks the best treatment for each patient
among a set of available treatment methods. Because a specific treatment does not work …