3 citations · 3 across the 7 of their papers we have counts for
8 papers
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…
Scalable LinUCB: Low-Rank Design Matrix Updates for Recommenders with Large Action Spaces
Evgenia Shustova, Marina Sheshukova, Sergey Samsonov +1
In this paper, we introduce PSI-LinUCB, a scalable variant of LinUCB that enables efficient training, inference, and memory usage by representing the inverse regularized design mat…
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…
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…
Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation
Marina Sheshukova, Denis Belomestny, Alain Durmus +3
We address the problem of solving strongly convex and smooth minimization problems using stochastic gradient descent (SGD) algorithm with a constant step size. Previous works sugge…
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova +3
This paper delves into stochastic optimization problems that involve Markovian noise. We present a unified approach for the theoretical analysis of first-order gradient methods for…
Rosenthal-type inequalities for linear statistics of Markov chains
Alain Durmus, Eric Moulines, Alexey Naumov +2
In this paper, we establish novel concentration inequalities for additive functionals of geometrically ergodic Markov chains similar to Rosenthal inequalities for sums of independe…