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05/25/2023
Federated Composite Saddle Point Optimization
Federated learning (FL) approaches for saddle point problems (SPP) have ...
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04/17/2023
Beyond first-order methods for non-convex non-concave min-max optimization
We propose a study of structured non-convex non-concave min-max problems...
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12/12/2022
Variance-Reduced Conservative Policy Iteration
We study the sample complexity of reducing reinforcement learning to a s...
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05/12/2022
Optimal Methods for Higher-Order Smooth Monotone Variational Inequalities
In this work, we present new simple and optimal algorithms for solving t...
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10/07/2021
A Stochastic Newton Algorithm for Distributed Convex Optimization
We propose and analyze a stochastic Newton algorithm for homogeneous dis...
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07/06/2021
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization
We provide several algorithms for constrained optimization of a large cl...
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02/13/2021
Almost-linear-time Weighted â„“_p-norm Solvers in Slightly Dense Graphs via Sparsification
We give almost-linear-time algorithms for constructing sparsifiers with ...
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02/02/2021
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication
We resolve the min-max complexity of distributed stochastic convex optim...
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07/09/2020
Higher-order methods for convex-concave min-max optimization and monotone variational inequalities
We provide improved convergence rates for constrained convex-concave min...
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02/18/2020
Is Local SGD Better than Minibatch SGD?
We study local SGD (also known as parallel SGD and federated averaging),...
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06/04/2019
Higher-Order Accelerated Methods for Faster Non-Smooth Optimization
We provide improved convergence rates for various non-smooth optimizatio...
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02/23/2019
Online Control with Adversarial Disturbances
We study the control of a linear dynamical system with adversarial distu...
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06/08/2018
The Case for Full-Matrix Adaptive Regularization
Adaptive regularization methods come in diagonal and full-matrix variant...
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10/27/2017
Not-So-Random Features
We propose a principled method for kernel learning, which relies on a Fo...
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11/03/2016
Finding Approximate Local Minima Faster than Gradient Descent
We design a non-convex second-order optimization algorithm that is guara...
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02/12/2016
