34 citations · 56 across the 7 of their papers we have counts for
7 papers
Sharper Exponential Convergence Rates for Sinkhorn's Algorithm in Continuous Settings
Lénaïc Chizat, Alex Delalande, Tomas Vaškevičius
We study the convergence rate of Sinkhorn's algorithm for solving entropy-regularized optimal transport problems when at least one of the probability measures, , admits a densit…
Computational Guarantees for Doubly Entropic Wasserstein Barycenters via Damped Sinkhorn Iterations
Lénaïc Chizat, Tomas Vaškevičius
We study the computation of doubly regularized Wasserstein barycenters, a recently introduced family of entropic barycenters governed by inner and outer regularization strengths. P…
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition
Varun Kanade, Patrick Rebeschini, Tomas Vaskevicius
The local Rademacher complexity framework is one of the most successful general-purpose toolboxes for establishing sharp excess risk bounds for statistical estimators based on the…
Distribution-Free Robust Linear Regression
Jaouad Mourtada, Tomas Vaškevičius, Nikita Zhivotovskiy
We study random design linear regression with no assumptions on the distribution of the covariates and with a heavy-tailed response variable. In this distribution-free regression s…
Suboptimality of Constrained Least Squares and Improvements via Non-Linear Predictors
Tomas Vaškevičius, Nikita Zhivotovskiy
We study the problem of predicting as well as the best linear predictor in a bounded Euclidean ball with respect to the squared loss. When only boundedness of the data generating d…
The Statistical Complexity of Early-Stopped Mirror Descent
Tomas Vaškevičius, Varun Kanade, Patrick Rebeschini
Recently there has been a surge of interest in understanding implicit regularization properties of iterative gradient-based optimization algorithms. In this paper, we study the sta…
Implicit Regularization for Optimal Sparse Recovery
Tomas Vaškevičius, Varun Kanade, Patrick Rebeschini
We investigate implicit regularization schemes for gradient descent methods applied to unpenalized least squares regression to solve the problem of reconstructing a sparse signal f…