research
∙
02/08/2023
Non-Stationary Bandits with Knapsack Problems with Advice
We consider a non-stationary Bandits with Knapsack problem. The outcome ...
share
research
∙
10/16/2021
On the Pareto Frontier of Regret Minimization and Best Arm Identification in Stochastic Bandits
We study the Pareto frontier of two archetypal objectives in stochastic ...
research
∙
10/15/2020
Probabilistic Sequential Shrinking: A Best Arm Identification Algorithm for Stochastic Bandits with Corruptions
We consider a best arm identification (BAI) problem for stochastic bandi...
research
∙
06/24/2020
Reinforcement Learning for Non-Stationary Markov Decision Processes: The Blessing of (More) Optimism
We consider un-discounted reinforcement learning (RL) in Markov decision...
research
∙
01/23/2020
Best Arm Identification for Cascading Bandits in the Fixed Confidence Setting
We design and analyze CascadeBAI, an algorithm for finding the best set ...
research
∙
06/07/2019
Reinforcement Learning under Drift
We propose algorithms with state-of-the-art dynamic regret bounds for un...
research
∙
05/15/2019
Exploration-Exploitation Trade-off in Reinforcement Learning on Online Markov Decision Processes with Global Concave Rewards
We consider an agent who is involved in a Markov decision process and re...
research
∙
03/04/2019
Hedging the Drift: Learning to Optimize under Non-Stationarity
We introduce general data-driven decision-making algorithms that achieve...
research
∙
10/11/2018
Inventory Balancing with Online Learning
We study a general problem of allocating limited resources to heterogene...
research
∙
10/06/2018
Learning to Optimize under Non-Stationarity
We introduce algorithms that achieve state-of-the-art dynamic regret bou...
research
∙
10/02/2018