Dmitriy Drusvyatskiy | DeepAI

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∙ 06/05/2023

Aiming towards the minimizers: fast convergence of SGD for overparametrized problems

Modern machine learning paradigms, such as deep learning, occur in or cl...

7 Chaoyue Liu, et al. ∙

∙ 01/16/2023

Asymptotic normality and optimality in nonsmooth stochastic approximation

In their seminal work, Polyak and Juditsky showed that stochastic approx...

9 Damek Davis, et al. ∙

∙ 07/09/2022

Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality

We analyze a stochastic approximation algorithm for decision-dependent p...

0 Joshua Cutler, et al. ∙

∙ 04/08/2022

Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments

This paper studies the problem of expected loss minimization given a dat...

0 Mitas Ray, et al. ∙

∙ 03/07/2022

Flat minima generalize for low-rank matrix recovery

Empirical evidence suggests that for a variety of overparameterized nonl...

0 Lijun Ding, et al. ∙

∙ 01/10/2022

Multiplayer Performative Prediction: Learning in Decision-Dependent Games

Learning problems commonly exhibit an interesting feedback mechanism whe...

0 Adhyyan Narang, et al. ∙

∙ 11/18/2021

Improved rates for derivative free play in convex games

The influential work of Bravo et al. 2018 shows that derivative free pla...

0 Dmitriy Drusvyatskiy, et al. ∙

∙ 08/26/2021

Subgradient methods near active manifolds: saddle point avoidance, local convergence, and asymptotic normality

Nonsmooth optimization problems arising in practice tend to exhibit bene...

0 Damek Davis, et al. ∙

∙ 08/16/2021

Stochastic optimization under time drift: iterate averaging, step decay, and high probability guarantees

We consider the problem of minimizing a convex function that is evolving...

0 Joshua Cutler, et al. ∙

∙ 06/17/2021

Escaping strict saddle points of the Moreau envelope in nonsmooth optimization

Recent work has shown that stochastically perturbed gradient methods can...

0 Damek Davis, et al. ∙

∙ 12/16/2019

Active strict saddles in nonsmooth optimization

We introduce a geometrically transparent strict saddle property for nons...

0 Damek Davis, et al. ∙

∙ 07/31/2019

Robust stochastic optimization with the proximal point method

Standard results in stochastic convex optimization bound the number of s...

0 Damek Davis, et al. ∙

∙ 07/22/2019

Stochastic algorithms with geometric step decay converge linearly on sharp functions

Stochastic (sub)gradient methods require step size schedule tuning to pe...

0 Damek Davis, et al. ∙

∙ 04/22/2019

Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence

The task of recovering a low-rank matrix from its noisy linear measureme...

0 Vasileios Charisopoulos, et al. ∙

∙ 01/06/2019

Composite optimization for robust blind deconvolution

The blind deconvolution problem seeks to recover a pair of vectors from ...

0 Vasileios Charisopoulos, et al. ∙

∙ 10/17/2018

Uniform Graphical Convergence of Subgradients in Nonconvex Optimization and Learning

We investigate the stochastic optimization problem of minimizing populat...

0 Damek Davis, et al. ∙

∙ 07/01/2018

Stochastic model-based minimization under high-order growth

Given a nonsmooth, nonconvex minimization problem, we consider algorithm...

0 Damek Davis, et al. ∙

∙ 04/20/2018

Stochastic subgradient method converges on tame functions

This work considers the question: what convergence guarantees does the s...

0 Damek Davis, et al. ∙

∙ 03/17/2018

Stochastic model-based minimization of weakly convex functions

We consider an algorithm that successively samples and minimizes stochas...

0 Damek Davis, et al. ∙

∙ 02/08/2018

Stochastic subgradient method converges at the rate O(k^-1/4) on weakly convex functions

We prove that the projected stochastic subgradient method, applied to a ...

0 Damek Davis, et al. ∙

∙ 03/31/2017

Catalyst Acceleration for Gradient-Based Non-Convex Optimization

We introduce a generic scheme to solve nonconvex optimization problems u...

0 Courtney Paquette, et al. ∙

∙ 01/19/2016

Variable projection without smoothness

The variable projection technique solves structured optimization problem...

0 Aleksandr Aravkin, et al. ∙

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