Blake Woodworth | DeepAI

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∙ 02/07/2023

Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy

We present an algorithm for minimizing an objective with hard-to-compute...

0 Blake Woodworth, et al. ∙

∙ 06/15/2022

Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays

The existing analysis of asynchronous stochastic gradient descent (SGD) ...

0 Konstantin Mishchenko, et al. ∙

∙ 04/11/2022

Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares

We consider potentially non-convex optimization problems, for which opti...

0 Blake Woodworth, 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. ∙

∙ 09/01/2021

The Minimax Complexity of Distributed Optimization

In this thesis, I study the minimax oracle complexity of distributed sto...

0 Blake Woodworth, et al. ∙

∙ 06/04/2021

An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning

We present and analyze an algorithm for optimizing smooth and convex or ...

0 Blake Woodworth, et al. ∙

∙ 02/19/2021

On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent

Recent work has highlighted the role of initialization scale in determin...

0 Shahar Azulay, 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. ∙

∙ 07/13/2020

Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy

We provide a detailed asymptotic study of gradient flow trajectories and...

0 Edward Moroshko, et al. ∙

∙ 06/08/2020

Minibatch vs Local SGD for Heterogeneous Distributed Learning

We analyze Local SGD (aka parallel or federated SGD) and Minibatch SGD i...

0 Blake Woodworth, et al. ∙

∙ 04/02/2020

Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow

We present a direct (primal only) derivation of Mirror Descent as a "par...

0 Suriya Gunasekar, et al. ∙

∙ 02/20/2020

Kernel and Rich Regimes in Overparametrized Models

A recent line of work studies overparametrized neural networks in the "k...

0 Blake Woodworth, 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. ∙

∙ 12/05/2019

Lower Bounds for Non-Convex Stochastic Optimization

We lower bound the complexity of finding ϵ-stationary points (with gradi...

0 Yossi Arjevani, et al. ∙

∙ 11/06/2019

The gradient complexity of linear regression

We investigate the computational complexity of several basic linear alge...

14 Mark Braverman, et al. ∙

∙ 07/01/2019

Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory

We note that known methods achieving the optimal oracle complexity for f...

0 Blake Woodworth, et al. ∙

∙ 06/21/2019

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis

We design a general framework for answering adaptive statistical queries...

0 Ryan Rogers, et al. ∙

∙ 06/13/2019

Kernel and Deep Regimes in Overparametrized Models

A recent line of work studies overparametrized neural networks in the "k...

0 Blake Woodworth, et al. ∙

∙ 02/13/2019

The Complexity of Making the Gradient Small in Stochastic Convex Optimization

We give nearly matching upper and lower bounds on the oracle complexity ...

0 Dylan Foster, et al. ∙

∙ 06/29/2018

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

Classifiers can be trained with data-dependent constraints to satisfy fa...

0 Andrew Cotter, et al. ∙

∙ 05/25/2018

Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization

We suggest a general oracle-based framework that captures different para...

0 Blake Woodworth, et al. ∙

∙ 03/12/2018

The Everlasting Database: Statistical Validity at a Fair Price

The problem of handling adaptivity in data analysis, intentional or not,...

0 Blake Woodworth, et al. ∙

∙ 05/25/2017

Implicit Regularization in Matrix Factorization

We study implicit regularization when optimizing an underdetermined quad...

0 Suriya Gunasekar, et al. ∙

∙ 05/25/2016

Tight Complexity Bounds for Optimizing Composite Objectives

We provide tight upper and lower bounds on the complexity of minimizing ...

0 Blake Woodworth, et al. ∙

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