Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

A search engine usually outputs a list of K web pages. The user examines this list, from the
first web page to the last, and chooses the first attractive page. This model of user behavior …

This paper studies the evaluation of policies that recommend an ordered set of items (eg, a
ranking) based on some context---a common scenario in web search, ads, and …

In the past few years, differential privacy has become a standard concept in the area of
privacy. One of the most important problems in this field is to answer queries while …

We consider un-discounted reinforcement learning (RL) in Markov decision processes
(MDPs) under drifting non-stationarity,\ie, both the reward and state transition distributions …

This paper investigates stochastic and adversarial combinatorial multi-armed bandit
problems. In the stochastic setting under semi-bandit feedback, we derive a problem-specific …

We study the application of the Thompson sampling (TS) methodology to the stochastic
combinatorial multi-armed bandit (CMAB) framework. We analyze the standard TS algorithm …

Abstract Meta-, multi-task, and federated learning can be all viewed as solving similar tasks,
drawn from a distribution that reflects task similarities. We provide a unified view of all these …

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 …