Brian Bullins | DeepAI

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∙ 05/25/2023

Federated Composite Saddle Point Optimization

Federated learning (FL) approaches for saddle point problems (SPP) have ...

0 Site Bai, et al. ∙

∙ 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...

0 Abhijeet Vyas, et al. ∙

∙ 12/12/2022

Variance-Reduced Conservative Policy Iteration

We study the sample complexity of reducing reinforcement learning to a s...

0 Naman Agarwal, et al. ∙

∙ 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...

0 Deeksha Adil, et al. ∙

∙ 10/07/2021

A Stochastic Newton Algorithm for Distributed Convex Optimization

We propose and analyze a stochastic Newton algorithm for homogeneous dis...

0 Brian Bullins, et al. ∙

∙ 07/06/2021

Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization

We provide several algorithms for constrained optimization of a large cl...

0 Deeksha Adil, et al. ∙

∙ 02/13/2021

Almost-linear-time Weighted ℓ_p-norm Solvers in Slightly Dense Graphs via Sparsification

We give almost-linear-time algorithms for constructing sparsifiers with ...

0 Deeksha Adil, et al. ∙

∙ 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...

0 Blake Woodworth, et al. ∙

∙ 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...

0 Brian Bullins, et al. ∙

∙ 02/18/2020

Is Local SGD Better than Minibatch SGD?

We study local SGD (also known as parallel SGD and federated averaging),...

5 Blake Woodworth, et al. ∙

∙ 06/04/2019

Higher-Order Accelerated Methods for Faster Non-Smooth Optimization

We provide improved convergence rates for various non-smooth optimizatio...

0 Brian Bullins, et al. ∙

∙ 02/23/2019

Online Control with Adversarial Disturbances

We study the control of a linear dynamical system with adversarial distu...

8 Naman Agarwal, et al. ∙

∙ 06/08/2018

The Case for Full-Matrix Adaptive Regularization

Adaptive regularization methods come in diagonal and full-matrix variant...

0 Naman Agarwal, et al. ∙

∙ 10/27/2017

Not-So-Random Features

We propose a principled method for kernel learning, which relies on a Fo...

0 Brian Bullins, et al. ∙

∙ 11/03/2016

Finding Approximate Local Minima Faster than Gradient Descent

We design a non-convex second-order optimization algorithm that is guara...

0 Naman Agarwal, et al. ∙

∙ 02/12/2016

Second-Order Stochastic Optimization for Machine Learning in Linear Time

First-order stochastic methods are the state-of-the-art in large-scale m...

0 Naman Agarwal, et al. ∙

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