Woodworth, Blake

13 publications

ICML 2025 Constant Stepsize Local GD for Logistic Regression: Acceleration by Instability Michael Crawshaw, Blake Woodworth, Mingrui Liu

ICLR 2025 Local Steps Speed up Local GD for Heterogeneous Distributed Logistic Regression Michael Crawshaw, Blake Woodworth, Mingrui Liu

ICML 2023 Two Losses Are Better than One: Faster Optimization Using a Cheaper Proxy Blake Woodworth, Konstantin Mishchenko, Francis Bach

COLT 2022 Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares Blake Woodworth, Francis Bach, Alessandro Rudi

AISTATS 2021 Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent Suriya Gunasekar, Blake Woodworth, Nathan Srebro

AISTATS 2020 Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis Ryan Rogers, Aaron Roth, Adam Smith, Nathan Srebro, Om Thakkar, Blake Woodworth

ICML 2020 Is Local SGD Better than Minibatch SGD? Blake Woodworth, Kumar Kshitij Patel, Sebastian Stich, Zhen Dai, Brian Bullins, Brendan Mcmahan, Ohad Shamir, Nathan Srebro

COLT 2020 Kernel and Rich 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 2019 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 J. Foster, Ayush Sekhari, Ohad Shamir, Nathan Srebro, Karthik Sridharan, Blake Woodworth

ICML 2019 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, Seungil You

COLT 2017 Learning Non-Discriminatory Predictors Blake Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro