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Found 6 papers, 2 papers with code
Corruption-Robust Offline Two-Player Zero-Sum Markov Games
We note that we are the first to provide such a characterization of the problem of learning approximate Nash Equilibrium policies in offline two-player zero-sum Markov games under data corruption.
Reward Model Learning vs. Direct Policy Optimization: A Comparative Analysis of Learning from Human Preferences
Moreover, we extend our analysis to the approximate optimization setting and derive exponentially decaying convergence rates for both RLHF and DPO.
Corruption Robust Offline Reinforcement Learning with Human Feedback
We aim to design algorithms that identify a near-optimal policy from the corrupted data, with provable guarantees.
Contextual Combinatorial Bandits with Changing Action Sets via Gaussian Processes
We consider a contextual bandit problem with a combinatorial action set and time-varying base arm availability.
Contextual Combinatorial Volatile Multi-armed Bandit with Adaptive Discretization
We consider contextual combinatorial volatile multi-armed bandit (CCV-MAB), in which at each round, the learner observes a set of available base arms and their contexts, and then, selects a super arm that contains $K$ base arms in order to maximize its cumulative reward.
Pareto Active Learning with Gaussian Processes and Adaptive Discretization
We consider the problem of optimizing a vector-valued objective function $\boldsymbol{f}$ sampled from a Gaussian Process (GP) whose index set is a well-behaved, compact metric space $({\cal X}, d)$ of designs.
