21 citations · 34 across the 9 of their papers we have counts for
15 papers
An Improved Model-Free Decision-Estimation Coefficient with Applications in Adversarial MDPs
Haolin Liu, Chen-Yu Wei, Julian Zimmert
We study decision making with structured observation (DMSO). Previous work (Foster et al., 2021b, 2023a) has characterized the complexity of DMSO via the decision-estimation coeffi…
Decision Making in Hybrid Environments: A Model Aggregation Approach
Haolin Liu, Chen-Yu Wei, Julian Zimmert
Recent work by Foster et al. (2021, 2022, 2023b) and Xu and Zeevi (2023) developed the framework of decision estimation coefficient (DEC) that characterizes the complexity of gener…
Beating Adversarial Low-Rank MDPs with Unknown Transition and Bandit Feedback
Haolin Liu, Zakaria Mhammedi, Chen-Yu Wei +1
We consider regret minimization in low-rank MDPs with fixed transition and adversarial losses. Previous work has investigated this problem under either full-information loss feedba…
Incentive-compatible Bandits: Importance Weighting No More
Julian Zimmert, Teodor V. Marinov
We study the problem of incentive-compatible online learning with bandit feedback. In this class of problems, the experts are self-interested agents who might misrepresent their pr…
Optimal cross-learning for contextual bandits with unknown context distributions
Jon Schneider, Julian Zimmert
We consider the problem of designing contextual bandit algorithms in the ``cross-learning'' setting of Balseiro et al., where the learner observes the loss for the action they play…
Towards Optimal Regret in Adversarial Linear MDPs with Bandit Feedback
Haolin Liu, Chen-Yu Wei, Julian Zimmert
We study online reinforcement learning in linear Markov decision processes with adversarial losses and bandit feedback, without prior knowledge on transitions or access to simulato…