blakewoodworth (at) gmail (dot) com

I am starting as an Assistant Professor in Computer Science at George Washington University in August 2023, and I will be accepting students interested in optimization and machine learning.

The primary focus of my research is on the theory of optimization, with a particular emphasis on precisely understanding convex, non-convex, and distributed optimization algorithms. I have also been very interested in efforts to understand modern, highly overparametrized machine learning models through the lens of implicit regularization. During my PhD, I also had the opportunity to work on fairness in ML and on adaptive data analysis.

Since October 2021, I have been a postdoctoral researcher with the SIERRA team at Inria, working with Francis Bach, and before that, I was a PhD student in computer science at the Toyota Technological Institute at Chicago (TTIC) advised by Nati Srebro until I graduated in Summer 2021. Before TTIC, I studied at Yale University where I received a B.S. in computer science, advised by Dan Spielman. At Yale, my coursework was spread evenly across the computer science, mathematics, and statistics departments; I was also a peer tutor for several programming-intensive computer science courses.

From September 2017-July 2019 I was supported by a NSF Graduate Research Fellowship, and from July 2019-August 2021, I was supported by a Google PhD Fellowship in machine learning.

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

Blake Woodworth, Konstantin Mishchenko, Francis Bach

*ICML, 2023*

**Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays**

Konstantin Mishchenko, Francis Bach, Mathieu Even, Blake Woodworth

*NeurIPS, 2022*

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

Blake Woodworth, Francis Bach, Alessandro Rudi

*COLT, 2022*

**Lower Bounds for Non-Convex Stochastic Optimization**

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

*Mathematical Programming, 2022*

*arXiv version*

**The Minimax Complexity of Distributed Optimization**

Blake Woodworth

*PhD Thesis, 2021*

**A Stochastic Newton Algorithm for Distributed Convex Optimization**

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

*NeurIPS, 2021*

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

Blake Woodworth, Nathan Srebro

*NeurIPS, 2021*

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

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

*ICML, 2021*

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

Blake Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro

Best Paper Award

*COLT, 2021*

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

Suriya Gunasekar, Blake Woodworth, Nathan Srebro

*AISTATS, 2021*

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

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

*NeurIPS, 2020*

**Minibatch vs Local SGD for Heterogeneous Distributed Learning**

Blake Woodworth, Kumar Kshitij Patel, Nathan Srebro

*NeurIPS, 2020*

**Is Local SGD Better than Minibatch SGD?**

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

*ICML, 2020*

Python code

**Kernel and Deep Regimes in Overparametrized Models**

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

*COLT, 2020*

**The Gradient Complexity of Linear Regression**

Mark Braverman, Elad Hazan, Max Simchowitz, Blake Woodworth

*COLT, 2020*

**Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis**

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

*AISTATS, 2020*

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

Blake Woodworth, Nathan Srebro

*COLT, 2019*

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

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

Best Student Paper Award

*COLT, 2019*

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

Blake Woodworth, Jialei Wang, Adam Smith, Brendan McMahan, and Nathan Srebro

*NeurIPS, 2018*

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

Andrew Cotter, Maya Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake Woodworth, and Seungil You

*FAT/ML 2018, ICML 2019*

**The Everlasting Database: Statistical Validity at a Fair Price**

Blake Woodworth, Vitaly Feldman, Saharon Rosset, and Nathan Srebro

*NeurIPS, 2018*

**Lower Bound for Randomized First Order Convex Optimization**

Blake Woodworth and Nathan Srebro

*arXiv, 2017*

**Implicit Regularization in Matrix Factorization**

Suriya Gunasekar, Blake Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, and Nathan Srebro

*NeurIPS, 2017*

**Learning Non-Discriminatory Predictors**

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

*COLT, 2017*

**Tight Complexity Bounds for Optimizing Composite Objectives**

Blake Woodworth and Nathan Srebro

*NeurIPS, 2016*

Last Updated: March 31, 2023