3 papers
cs.LG2026
Efficient Hypergradient Descent for Inverse Reinforcement Learning
Nikita Sevriukov, Anna Barabanova, Uliana Gagarina +4
Inverse reinforcement learning (IRL) aims to recover a reward function under which the resulting policy reproduces the behavior observed in expert demonstrations. A natural approac…
stat.ML2026
Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
Ilya Levin, Maksim Shuklin, Eric Moulines +2
In this paper, we establish Berry-Esseen-type bounds for federated linear stochastic approximation (LSA). Our results provide the first federated Gaussian approximations for LSA th…
stat.ML2025
High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation
Ilya Levin, Alexey Naumov, Sergey Samsonov
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 foc…