Author

Sergey Samsonov

5 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

#machine learning Conference Aug 2025

High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation

In this paper, we study the bias and high-order error bounds of the Linear Stochastic Approximation (LSA) algorithm with Polyak-Ruppert (PR) averaging under Markovian noise. We focus on the version of the algorithm with constant step size and propose a novel decomposition of the bias via a linearization technique. We a...

I. Levin, Alexey Naumov, Sergey Samsonov · AAAI Conference on Artificia... · 2 citations

Preprint Oct 2026

Effective-dimension bounds in the central limit theorem for centered Euclidean balls

We establish a nonasymptotic Gaussian approximation bound over centered Euclidean balls for sums of independent, not necessarily identically distributed, random vectors in $\mathbb R^p$. Let $S_n$ denote the sum, let $\Sigma=\operatorname{Var}(S_n)$, and let $G\sim N(0,\Sigma)$. Define the effective rank and th...

A. Naumov, Sergey Samsonov, Vladimir Spokoiny et al. · 0 citations

Preprint Aug 2026

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

Conformal prediction endows arbitrary black-box predictors with finite-sample, distribution-free marginal coverage, yet marginal validity can hide severe covariate-specific miscalibration, while exact distribution-free conditional coverage is finite-sample unattainable. Randomly localized conformal prediction (RLCP) mi...

Anton Conrad, R. Isaev, D. Belomestny et al. · 0 citations

#machine learning Preprint Sep 2026

Conformalized Quantile Regression and Minimax Limits of Fixed-Score Calibration under Known Covariate Shift

In this paper, we study nonasymptotic $L^p$ error bounds for interval length and conditional coverage in split conformalized quantile regression (CQR). Our bounds rely on local regularity conditions and accuracy guarantees for the estimated quantiles. We further instantiate our bounds for quantile regression with spars...

R. Isaev, Anton Conrad, D. Belomestny et al. · 0 citations

#machine learning Dec 2025

Statistical analysis of Inverse Entropy-regularized Reinforcement Learning

A statistical framework for Inverse Entropy-regularized Reinforcement Learning is developed that resolves ambiguity by combining entropy regularization with a least-squares reconstruction of the reward from the soft Bellman residual, leading to a unique and well-defined so-called least-squares reward consistent with th...

D. Belomestny, Alexey Naumov, Sergey Samsonov · arXiv.org · 0 citations