1 citations · 3 across the 19 of their papers we have counts for
4 papers · 1 filter
Adapting to Stochastic and Adversarial Losses in Episodic MDPs with Aggregate Bandit Feedback
Shinji Ito, Kevin Jamieson, Haipeng Luo +2
We study online learning in finite-horizon episodic Markov decision processes (MDPs) under the challenging aggregate bandit feedback model, where the learner observes only the cumu…
On the Universal Near Optimality of Hedge in Combinatorial Settings
Zhiyuan Fan, Arnab Maiti, Kevin Jamieson +2
In this paper, we study the classical Hedge algorithm in combinatorial settings. In each round, the learner selects a vector from a set ,…
Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals
Junyan Liu, Arnab Maiti, Artin Tajdini +2
We initiate the study of a repeated principal-agent problem over a finite horizon , where a principal sequentially interacts with types of agents arriving in an advers…
Efficient Near-Optimal Algorithm for Online Shortest Paths in Directed Acyclic Graphs with Bandit Feedback Against Adaptive Adversaries
Arnab Maiti, Zhiyuan Fan, Kevin Jamieson +2
In this paper, we study the online shortest path problem in directed acyclic graphs (DAGs) under bandit feedback against an adaptive adversary. Given a DAG with a sour…