Distributional shifts pose a significant challenge to achieving robustne...
We consider the Pareto set identification (PSI) problem in multi-objecti...
Multi-armed bandits (MAB) is a simple reinforcement learning model where...
Multi-armed bandits (MAB) are extensively studied in various settings wh...
In many real-world applications of combinatorial bandits such as content...
Finding an optimal individualized treatment regimen is considered one of...
We introduce vector optimization problems with stochastic bandit feedbac...
We consider a contextual bandit problem with a combinatorial action set ...
In high-stakes applications of data-driven decision making like healthca...
We consider the problem of optimizing a vector-valued objective function...
We consider a multiobjective multiarmed bandit problem with lexicographi...
Influence maximization, item recommendation, adaptive routing and dynami...
Many sequential decision-making tasks require choosing at each decision ...
Machine learning models trained on data from multiple demographic groups...
We analyze the regret of combinatorial Thompson sampling (CTS) for the
c...
In this paper, we introduce the COmbinatorial Multi-Objective Multi-Arme...
In this paper we propose the multi-objective contextual bandit problem w...
In this paper, we study the combinatorial multi-armed bandit problem (CM...
Extracting actionable intelligence from distributed, heterogeneous,
corr...
In this paper, we introduce a new class of reinforcement learning method...
Recommender systems, medical diagnosis, network security, etc., require
...
In this paper, we consider decentralized sequential decision making in
d...
In this paper we propose a novel framework for decentralized, online lea...
Distributed, online data mining systems have emerged as a result of
appl...
In an online contract selection problem there is a seller which offers a...