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stat.ML2026

Beyond Marginal Validity: Finite-Sample Guarantees for Localized Conformal Prediction

Anton Conrad, Rustam Isaev, Denis Belomestny +2

Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscali…

stat.ML2026

Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

Marina Sheshukova, Sergey Samsonov, Denis Belomestny +4

In this paper, we establish the non-asymptotic validity of the multiplier bootstrap procedure for constructing the confidence sets using the Stochastic Gradient Descent (SGD) algor…

stat.ML2026

Proximal Point Nash Learning from Human Feedback

Daniil Tiapkin, Daniele Calandriello, Denis Belomestny +5

Traditional Reinforcement Learning from Human Feedback (RLHF) often relies on reward models, frequently assuming preference structures like the Bradley--Terry model, which may not…

stat.ML2026

Schrödinger bridge problem via empirical risk minimization

Denis Belomestny, Alexey Naumov, Nikita Puchkin +1

We study the Schrödinger bridge problem when the endpoint distributions are available only through samples. Classical computational approaches estimate Schrödinger potentials via…

stat.ML2025

Statistical analysis of Inverse Entropy-regularized Reinforcement Learning

Denis Belomestny, Alexey Naumov, Sergey Samsonov

Inverse reinforcement learning aims to infer the reward function that explains expert behavior observed through trajectories of state--action pairs. A long-standing difficulty in c…