arXiv Machine Learning
Jul 9
Finding a stationary point of a stochastic convex problem
arXiv:2607. 06883v1 Announce Type: cross Abstract: We consider the problem of finding stationary points for stochastic convex optimization problems.
Random tilts to find stationary points in stochastic convex optimization
arXiv:2609. 17798v1 Announce Type: cross Abstract: We consider the problem of finding stationary points of stochastic convex functions and related variational inequalities.
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arXiv Machine Learning
Aug 10
A proximal subgradient method for nonconvex stochastic optimization under the Kurdyka-{\L}ojasiewicz condition
arXiv:2608. 05460v1 Announce Type: cross Abstract: This work introduces a proximal stochastic subgradient method for minimizing the sum of an expected cost, whose integrand is potentially nonsmooth and nonconvex, and a lower semicontinuous, prox-bounded function.
arXiv Machine Learning
Jul 2
Towards Weaker Variance Assumptions for Stochastic Optimization
arXiv:2504. 09951v2 Announce Type: replace-cross Abstract: We revisit a classical assumption for analyzing stochastic gradient algorithms where the squared norm of the stochastic subgradient (or the variance for smooth problems) is allowed to grow as fast as the squared norm of the optimization variable.
arXiv Machine Learning
Sep 10
How to Make the Gradient Mapping Small for Constrained Stochastic Min-Max Problems and Beyond
arXiv:2609.08380v1 Announce Type: cross Abstract: We study the stochastic first-order oracle complexity for constrained or regularized convex-concave min-max optimization and stochastic monotone vari...
arXiv Machine Learning
Jun 15
Lower Complexity Bounds for Nonconvex-Strongly-Convex Bilevel Optimization with First-Order Oracles
arXiv:2511. 19656v3 Announce Type: replace Abstract: Although upper bound guarantees for bilevel optimization have been widely studied, progress on lower bounds has been limited due to the complexity of the bilevel structure.
arXiv Machine Learning
Jul 28
Risk reversal for least squares estimators under nested convex constraints
arXiv:2601. 16041v2 Announce Type: replace-cross Abstract: In constrained stochastic optimization, one expects that restricting the feasible set, provided it still contains the true parameter, should not increase the statistical risk of the corresponding projection estimator.
arXiv Machine Learning
Jun 24
New Bounds for the Last Iterate of the Stochastic subGradient Method
arXiv:2606. 24879v1 Announce Type: cross Abstract: We study the last iterate of the stochastic subgradient method for one-dimensional convex Lipschitz objectives.
arXiv Machine Learning
Aug 7
On Same-Sample and Independent-Sample Stochastic Extragradient for Monotone Variational Inequalities
arXiv:2608. 06182v1 Announce Type: cross Abstract: We study stochastic extragradient (SEG) methods for solving monotone variational inequality problems (VIPs) over a feasible set.
arXiv Machine Learning
Jun 2
In-Expectation Convergence of Stochastic Gradient Methods under Heavy-Tailed Noise
arXiv:2606. 00520v1 Announce Type: cross Abstract: Many stochastic gradient methods are believed not to converge when the noise in stochastic gradients has only a finite $p$-th moment for $p\in\left(1,2\right)$, a setting known as the heavy-tailed noise assumption.
arXiv Machine Learning
Jun 25
A Geometry-Aware Efficient Algorithm for Compositional Entropic Risk Minimization
arXiv:2602. 02877v2 Announce Type: replace Abstract: This paper studies optimization for a family of problems termed $\textbf{compositional entropic risk minimization}$, in which each data's loss is formulated as a Log-Expectation-Exponential (Log-E-Exp) function.
arXiv Machine Learning
Sep 22
Accelerated stochastic first-order method for convex optimization under heavy-tailed noise
arXiv:2510.11676v2 Announce Type: replace-cross Abstract: We study convex composite optimization problems, where the objective function is given by the sum of a prox-friendly function and a convex fu...
arXiv Statistics ML
Aug 26
Lions and Muons: Optimization via Stochastic Frank-Wolfe under Heavy-Tailed Noise
arXiv:2506.04192v4 Announce Type: replace-cross Abstract: Stochastic Frank-Wolfe is a classical optimization method for solving constrained optimization problems. On the other hand, recent optimizers...