8 citations · 31 across the 37 of their papers we have counts for
7 papers · 2 filters
Statistical analysis of Inverse Entropy-regularized Reinforcement Learning
Denis Belomestny, Alexey Naumov, Artemy Rubtsov +1
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…
Improved Central Limit Theorem and Bootstrap Approximations for Linear Stochastic Approximation
Bogdan Butyrin, Eric Moulines, Alexey Naumov +3
In this paper, we refine the Berry-Esseen bounds for the multivariate normal approximation of Polyak-Ruppert averaged iterates arising from the linear stochastic approximation (LSA…
On the Rate of Gaussian Approximation for Linear Regression Problems
Marat Khusainov, Marina Sheshukova, Alain Durmus +1
In this paper, we consider the problem of Gaussian approximation for the online linear regression task. We derive the corresponding rates for the setting of a constant learning rat…
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…
Gaussian Approximation for Two-Timescale Linear Stochastic Approximation
Bogdan Butyrin, Artemy Rubtsov, Alexey Naumov +2
In this paper, we establish non-asymptotic bounds for accuracy of normal approximation for linear two-timescale stochastic approximation (TTSA) algorithms driven by martingale diff…
Statistical inference for Linear Stochastic Approximation with Markovian Noise
Sergey Samsonov, Marina Sheshukova, Eric Moulines +1
In this paper we derive non-asymptotic Berry-Esseen bounds for Polyak-Ruppert averaged iterates of the Linear Stochastic Approximation (LSA) algorithm driven by the Markovian noise…
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…