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Found 4 papers, 1 papers with code
Optimal Regret with Limited Adaptivity for Generalized Linear Contextual Bandits
For our batch learning algorithm B-GLinCB, with $\Omega\left( \log{\log T} \right)$ batches, the regret scales as $\tilde{O}(\sqrt{T})$.
Provably Sample Efficient RLHF via Active Preference Optimization
Experimental evaluations on a human preference dataset validate \texttt{APO}'s efficacy as a sample-efficient and practical solution to data collection for RLHF, facilitating alignment of LLMs with human preferences in a cost-effective and scalable manner.
Inverse Reinforcement Learning With Constraint Recovery
In this work, we propose a novel inverse reinforcement learning (IRL) algorithm for constrained Markov decision process (CMDP) problems.
A View Independent Classification Framework for Yoga Postures
We argue that for most of the applications, validation accuracies on unseen subjects and unseen camera angles would be most important.
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