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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...
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01/16/2023
Asymptotic normality and optimality in nonsmooth stochastic approximation
In their seminal work, Polyak and Juditsky showed that stochastic approx...
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07/09/2022
Stochastic approximation with decision-dependent distributions: asymptotic normality and optimality
We analyze a stochastic approximation algorithm for decision-dependent p...
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04/08/2022
Decision-Dependent Risk Minimization in Geometrically Decaying Dynamic Environments
This paper studies the problem of expected loss minimization given a dat...
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03/07/2022
Flat minima generalize for low-rank matrix recovery
Empirical evidence suggests that for a variety of overparameterized nonl...
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01/10/2022
Multiplayer Performative Prediction: Learning in Decision-Dependent Games
Learning problems commonly exhibit an interesting feedback mechanism whe...
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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...
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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...
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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...
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06/17/2021
Escaping strict saddle points of the Moreau envelope in nonsmooth optimization
Recent work has shown that stochastically perturbed gradient methods can...
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12/16/2019
Active strict saddles in nonsmooth optimization
We introduce a geometrically transparent strict saddle property for nons...
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07/31/2019
Robust stochastic optimization with the proximal point method
Standard results in stochastic convex optimization bound the number of s...
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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...
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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...
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01/06/2019
Composite optimization for robust blind deconvolution
The blind deconvolution problem seeks to recover a pair of vectors from ...
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10/17/2018
Uniform Graphical Convergence of Subgradients in Nonconvex Optimization and Learning
We investigate the stochastic optimization problem of minimizing populat...
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07/01/2018
Stochastic model-based minimization under high-order growth
Given a nonsmooth, nonconvex minimization problem, we consider algorithm...
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04/20/2018
Stochastic subgradient method converges on tame functions
This work considers the question: what convergence guarantees does the s...
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03/17/2018
Stochastic model-based minimization of weakly convex functions
We consider an algorithm that successively samples and minimizes stochas...
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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 ...
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03/31/2017
Catalyst Acceleration for Gradient-Based Non-Convex Optimization
We introduce a generic scheme to solve nonconvex optimization problems u...
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01/19/2016
