Blake E. Woodworth

According to our database, Blake E. Woodworth authored at least 29 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Lower bounds for non-convex stochastic optimization.

Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth

Math. Program., May, 2023

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

Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach

Proceedings of the International Conference on Machine Learning, 2023

2022

Towards Optimal Communication Complexity in Distributed Non-Convex Optimization.

Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays.

Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract).

Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

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

Blake E. Woodworth, Francis R. Bach, Alessandro Rudi

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021

The Minimax Complexity of Distributed Optimization.

Blake E. Woodworth

CoRR, 2021

A Field Guide to Federated Optimization.

Jianyu Wang, Zachary Charles, Zheng Xu, Gauri Joshi, H. Brendan McMahan, Blaise Agüera y Arcas, Maruan Al-Shedivat, Galen Andrew, Salman Avestimehr, Katharine Daly, Deepesh Data, Suhas N. Diggavi, Hubert Eichner, Advait Gadhikar, Zachary Garrett, Antonious M. Girgis, Filip Hanzely, Andrew Hard, Chaoyang He, Samuel Horváth, Zhouyuan Huo, Alex Ingerman, Martin Jaggi, Tara Javidi, Peter Kairouz, Satyen Kale, Sai Praneeth Karimireddy, Jakub Konecný, Sanmi Koyejo, Tian Li, Luyang Liu, Mehryar Mohri, Hang Qi, Sashank J. Reddi, Peter Richtárik, Karan Singhal, Virginia Smith, Mahdi Soltanolkotabi, Weikang Song, Ananda Theertha Suresh, Sebastian U. Stich, Ameet Talwalkar, Hongyi Wang, Blake E. Woodworth, Shanshan Wu, Felix X. Yu, Honglin Yuan, Manzil Zaheer, Mi Zhang, Tong Zhang, Chunxiang Zheng, Chen Zhu, Wennan Zhu

CoRR, 2021

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

Blake E. Woodworth, Nathan Srebro

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

A Stochastic Newton Algorithm for Distributed Convex Optimization.

Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry

Proceedings of the 38th International Conference on Machine Learning, 2021

The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication.

Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2021

Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent.

Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

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

Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro

CoRR, 2020

Minibatch vs Local SGD for Heterogeneous Distributed Learning.

Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Is Local SGD Better than Minibatch SGD?

Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro

Proceedings of the 37th International Conference on Machine Learning, 2020

Kernel and Rich Regimes in Overparametrized Models.

Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2020

The Gradient Complexity of Linear Regression.

Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth

Proceedings of the Conference on Learning Theory, 2020

Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis.

Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

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

Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Blake E. Woodworth, Nathan Srebro

Proceedings of the Conference on Learning Theory, 2019

The Complexity of Making the Gradient Small in Stochastic Convex Optimization.

Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth

Proceedings of the Conference on Learning Theory, 2019

2018

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

Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro

CoRR, 2018

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

Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Everlasting Database: Statistical Validity at a Fair Price.

Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Implicit Regularization in Matrix Factorization.

Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Learning Non-Discriminatory Predictors.

Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro

Proceedings of the 30th Conference on Learning Theory, 2017

2016

Tight Complexity Bounds for Optimizing Composite Objectives.

Blake E. Woodworth, Nati Srebro

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016