Author

M. Sheshukova

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#machine learning Preprint Oct 2026

Accelerating Linear Bandits with Persistent Candidate Sets

Exhaustive optimistic scoring can make action selection costly in linear bandits with a large fixed set of $K$ actions, requiring $\mathcal{O}(K)$ score evaluations per round. We introduce Persistent Candidate LinUCB (\textsc{PC-LinUCB}), which maintains $k$ active actions and samples $q$ challengers uniformly from the...

Bing-Kui Tong, Daria Korovaitceva, M. Sheshukova et al. · 0 citations

Preprint Aug 2026

Efficient Hypergradient Descent for Inverse Reinforcement Learning

This work shows that, at the inner optimum, the Hessian of the inner objective is proportional to the Fisher information matrix of the policy, yielding a structured Fisher-based hypergradient closely related to Natural Hypergradient Descent.

Nikita Sevriukov, A. Barabanova, Uliana Gagarina et al. · 0 citations