Blake E. Woodworth

Publication Activity (10 Years)

Years Active: 2016-2023
Publications (10 Years): 50

Top Topics

Tight Complexity Bounds

Convex Optimization

Top Venues

Publications

Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. CoRR (2023)
Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. ICML (2023)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
Lower bounds for non-convex stochastic optimization. Math. Program. 199 (1) (2023)
Blake E. Woodworth, Francis R. Bach, Alessandro Rudi
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. CoRR (2022)
Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. NeurIPS (2022)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication (Extended Abstract). IJCAI (2022)
Blake E. Woodworth, Francis R. Bach, Alessandro Rudi
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. COLT (2022)
Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. CoRR (2022)
Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. NeurIPS (2022)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR (2021)
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 Horvath, 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
A Field Guide to Federated Optimization. CoRR (2021)
Blake E. Woodworth, Nathan Srebro
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. NeurIPS (2021)
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS (2021)
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. CoRR (2021)
Blake E. Woodworth, Nathan Srebro
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. CoRR (2021)
Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT (2021)
Shahar Azulay, Edward Moroshko, Mor Shpigel Nacson, Blake E. Woodworth, Nathan Srebro, Amir Globerson, Daniel Soudry
On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent. ICML (2021)
Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. AISTATS (2021)
Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth
A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR (2021)
Blake E. Woodworth
The Minimax Complexity of Distributed Optimization. CoRR (2021)
Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro
Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow. CoRR (2020)
Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
Kernel and Rich Regimes in Overparametrized Models. CoRR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro
Minibatch vs Local SGD for Heterogeneous Distributed Learning. NeurIPS (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
Is Local SGD Better than Minibatch SGD? ICML (2020)
Edward Moroshko, Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Nati Srebro, Daniel Soudry
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. NeurIPS (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Nathan Srebro
Minibatch vs Local SGD for Heterogeneous Distributed Learning. CoRR (2020)
Edward Moroshko, Suriya Gunasekar, Blake E. Woodworth, Jason D. Lee, Nathan Srebro, Daniel Soudry
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy. CoRR (2020)
Blake E. Woodworth, Kumar Kshitij Patel, Sebastian U. Stich, Zhen Dai, Brian Bullins, H. Brendan McMahan, Ohad Shamir, Nathan Srebro
Is Local SGD Better than Minibatch SGD? CoRR (2020)
Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth
The Gradient Complexity of Linear Regression. COLT (2020)
Blake E. Woodworth, Suriya Gunasekar, Jason D. Lee, Edward Moroshko, Pedro Savarese, Itay Golan, Daniel Soudry, Nathan Srebro
Kernel and Rich Regimes in Overparametrized Models. COLT (2020)
Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. AISTATS (2020)
Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth
Lower Bounds for Non-Convex Stochastic Optimization. CoRR (2019)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. COLT (2019)
Blake E. Woodworth, Nathan Srebro
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. CoRR (2019)
Dylan J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake E. Woodworth
The Complexity of Making the Gradient Small in Stochastic Convex Optimization. CoRR (2019)
Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth
The gradient complexity of linear regression. CoRR (2019)
Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR (2019)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML (2019)
Blake E. Woodworth, Nathan Srebro
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. COLT (2019)
Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro
The Everlasting Database: Statistical Validity at a Fair Price. NeurIPS (2018)
Blake E. Woodworth, Jialei Wang, Adam D. Smith, Brendan McMahan, Nati Srebro
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. NeurIPS (2018)
Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Wang, Blake E. Woodworth, Seungil You
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR (2018)
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
Implicit Regularization in Matrix Factorization. ITA (2018)
Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. CoRR (2018)
Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro
The Everlasting Database: Statistical Validity at a Fair Price. CoRR (2018)
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro
Implicit Regularization in Matrix Factorization. CoRR (2017)
Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro
Learning Non-Discriminatory Predictors. COLT (2017)
Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro
Learning Non-Discriminatory Predictors. CoRR (2017)
Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro
Implicit Regularization in Matrix Factorization. NIPS (2017)
Blake E. Woodworth, Nati Srebro
Tight Complexity Bounds for Optimizing Composite Objectives. NIPS (2016)