15 citations · 36 across the 9 of their papers we have counts for
12 papers
Clipped or Unclipped? Finite-Sample Trade-offs for Averaged SGD under Heavy-Tailed Noise
Alexandra Suvorikova, Egor Gladin, Darina Dvinskikh +5
Gradient clipping is widely used to stabilize training, but it need not improve the statistical accuracy of averaged SGD, even under heavy-tailed noise. We derive a finite-sample c…
Wall-Clock Complexity for Zeroth-Order Optimization with Tunable Oracle Fidelity
Alexandra Suvorikova, Igor Pavlov, Artem Vasin +4
Zeroth-order (black-box) optimization is applied when gradients are unavailable and objective evaluations rely on expensive simulations. In many such applications, the oracle fidel…
SLDP: Semi-Local Differential Privacy for Density-Adaptive Analytics
Alexey Kroshnin, Alexandra Suvorikova
Density-adaptive domain discretization is essential for high-utility privacy-preserving analytics but remains challenging under Local Differential Privacy (LDP) due to the privacy-…
friends.test: rank-based method for feature selection in interaction matrices
Alexandra Suvorikova, Alexey Kroshnin, Dmirijs Lvovs +5
The analysis of the interaction matrix between two distinct sets is essential across diverse fields, from pharmacovigilance to transcriptomics. Not all interactions are equally inf…
Managing Self-Learning Experts under Per-Round Budget Constraints
Ilgam Latypov, Alexandra Suvorikova, Alexey Kroshnin +2
This paper addresses the problem of sequential decision-making under learning budget constraints. Such settings naturally arise in applications like managing a portfolio of bandit…
Bernstein-type and Bennett-type inequalities for unbounded matrix martingales
Alexey Kroshnin, Alexandra Suvorikova
We derive explicit Bernstein-type and Bennett-type concentration inequalities for matrix-valued martingale processes with unbounded observations from the Hermitian space $\mathbb{H…
Generalized bootstrap in the Bures-Wasserstein space
Alexey Kroshnin, Vladimir Spokoiny, Alexandra Suvorikova
This study proposes a bootstrap-based method for uncertainty quantification in two important statistical scenarios. First, we approximate the sampling distribution of empirical bar…
Statistical inference for Bures-Wasserstein barycenters
Alexey Kroshnin, Vladimir Spokoiny, Alexandra Suvorikova
In this work we introduce the concept of Bures-Wasserstein barycenter , that is essentially a Fréchet mean of some distribution supported on a subspace of positiv…
Multivariate Brenier cumulative distribution functions and their application to non-parametric testing
Melf Boeckel, Vladimir Spokoiny, Alexandra Suvorikova
In this work we introduce a novel approach of construction of multivariate cumulative distribution functions, based on cyclical-monotone mapping of an original measure $μ\in \mathc…
Gaussian processes with multidimensional distribution inputs via optimal transport and Hilbertian embedding
Francois Bachoc, Alexandra Suvorikova, David Ginsbourger +2
In this work, we investigate Gaussian Processes indexed by multidimensional distributions. While directly constructing radial positive definite kernels based on the Wasserstein dis…
Construction of Non-asymptotic Confidence Sets in 2-Wasserstein Space
Johannes Ebert, Vladimir Spokoiny, Alexandra Suvorikova
In this paper, we consider a probabilistic setting where the probability measures are considered to be random objects. We propose a procedure of construction non-asymptotic confide…
Universal method with inexact oracle and its applications for searching equillibriums in multistage transport problems
Alexander Gasnikov, Pavel Dvurechensky, Dmitry Kamzolov +5
In this paper we propose a new efficient approach for numerical calculation of equillibriums in multistage transport problems. In the very core of our approach lies the proper comb…