We consider a stochastic linear bandit model in which the available actions correspond to
arbitrary context vectors whose associated rewards follow a non-stationary linear regression …

We introduce data-driven decision-making algorithms that achieve state-of-the-art dynamic
regret bounds for a collection of nonstationary stochastic bandit settings. These settings …

An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills
over a long lifetime could advance the frontier of artificial intelligence capabilities. The …

Pre-trained multimodal foundation models have demonstrated remarkable generalizability
but pose challenges for deployment due to their large sizes. One effective approach to …

In nonstationary bandit learning problems, the decision-maker must continually gather
information and adapt their action selection as the latent state of the environment evolves. In …

The Multi-Armed Bandit (MAB) problem has been extensively studied in order to address
real-world challenges related to sequential decision making. In this setting, an agent selects …

A key feature of sequential decision making under uncertainty is a need to balance between
exploiting—choosing the best action according to the current knowledge, and exploring …

We study the recovering bandits problem, a variant of the stochastic multi-armed bandit
problem where the expected reward of each arm varies according to some unknown …

Thompson sampling has proven effective across a wide range of stationary bandit
environments. However, as we demonstrate in this paper, it can perform poorly when …

Multi-task learning (MTL) has achieved success over a wide range of problems, where the
goal is to improve the performance of a primary task using a set of relevant auxiliary tasks …