Blake Woodworth

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All published works

Action	Title	Year	Authors
+ PDF Chat	Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow in Shallow Linear Networks	2025	Pierfrancesco Beneventano Blake Woodworth
+ PDF Chat	Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy	2023	Blake Woodworth Konstantin Mishchenko Francis Bach
+	Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy	2023	Blake Woodworth Konstantin Mishchenko Francis Bach
+ PDF Chat	Lower bounds for non-convex stochastic optimization	2022	Yossi Arjevani Yair Carmon John C. Duchi Dylan J. Foster Nathan Srebro Blake Woodworth
+	Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares	2022	Blake Woodworth Francis Bach Alessandro Rudi
+	Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays	2022	Konstantin Mishchenko Francis Bach Mathieu Even Blake Woodworth
+	A Stochastic Newton Algorithm for Distributed Convex Optimization	2021	Brian Bullins Kumar Kshitij Patel Ohad Shamir Nathan Srebro Blake Woodworth
+	An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning	2021	Blake Woodworth Nathan Srebro
+	The Minimax Complexity of Distributed Optimization	2021	Blake Woodworth
+	An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning	2021	Blake Woodworth Nathan Srebro
+	The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication	2021	Blake Woodworth Brian Bullins Ohad Shamir Nathan Srebro
+	On the Implicit Bias of Initialization Shape: Beyond Infinitesimal Mirror Descent	2021	Shahar Azulay Edward Moroshko Mor Shpigel Nacson Blake Woodworth Nathan Srebro Amir Globerson Daniel Soudry
+	The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication	2021	Blake Woodworth Brian Bullins Ohad Shamir Nathan Srebro
+	A Field Guide to Federated Optimization	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
+	A Stochastic Newton Algorithm for Distributed Convex Optimization	2021	Brian Bullins Kumar Kshitij Patel Ohad Shamir Nathan Srebro Blake Woodworth
+	An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning	2021	Blake Woodworth Nathan Srebro
+	The Minimax Complexity of Distributed Optimization	2021	Blake Woodworth
+	Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy	2020	Edward Moroshko Blake Woodworth Suriya Gunasekar Jason D. Lee Nati Srebro Daniel Soudry
+	Guaranteed validity for empirical approaches to adaptive data analysis	2020	Ryan Rogers Aaron Roth Adam Smith Nathan Srebro Om Thakkar Blake Woodworth
+	The Gradient Complexity of Linear Regression	2020	Mark Braverman Elad Hazan Max Simchowitz Blake Woodworth
+	Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy	2020	Edward Moroshko Suriya Gunasekar Blake Woodworth Jason D. Lee Nathan Srebro Daniel Soudry
+	Minibatch vs Local SGD for Heterogeneous Distributed Learning	2020	Blake Woodworth Kumar Kshitij Patel Nathan Srebro
+	Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow.	2020	Suriya Gunasekar Blake Woodworth Nathan Srebro
+	Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent	2020	Suriya Gunasekar Blake Woodworth Nathan Srebro
+	Kernel and Rich Regimes in Overparametrized Models	2020	Blake Woodworth Suriya Gunasekar Jason D. Lee Edward Moroshko Pedro Savarese Itay Golan Daniel Soudry Nathan Srebro
+	Is Local SGD Better than Minibatch SGD?	2020	Blake Woodworth Kumar Kshitij Patel Sebastian U. Stich Zhen Dai Brian Bullins H. Brendan McMahan Ohad Shamir Nathan Srebro
+	Minibatch vs Local SGD for Heterogeneous Distributed Learning	2020	Blake Woodworth Kumar Kshitij Patel Nathan Srebro
+	Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy	2020	Edward Moroshko Suriya Gunasekar Blake Woodworth Jason D. Lee Nathan Srebro Daniel Soudry
+	Minibatch vs Local SGD for Heterogeneous Distributed Learning	2020	Blake Woodworth Kumar Kshitij Patel Nathan Srebro
+	Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent	2020	Suriya Gunasekar Blake Woodworth Nathan Srebro
+	Kernel and Rich Regimes in Overparametrized Models	2020	Blake Woodworth Suriya Gunasekar Jason D. Lee Edward Moroshko Pedro Savarese Itay Golan Daniel Soudry Nathan Srebro
+	Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory	2019	Blake Woodworth Nathan Srebro
+	The Complexity of Making the Gradient Small in Stochastic Convex Optimization	2019	Dylan J. Foster Ayush Sekhari Ohad Shamir Nathan Srebro Karthik Sridharan Blake Woodworth
+	Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis	2019	Ryan Rogers Aaron Roth Adam Smith Nathan Srebro Om Thakkar Blake Woodworth
+	Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory	2019	Blake Woodworth Nathan Srebro
+	The gradient complexity of linear regression	2019	Mark Braverman Elad Hazan Max Simchowitz Blake Woodworth
+	Lower Bounds for Non-Convex Stochastic Optimization	2019	Yossi Arjevani Yair Carmon John C. Duchi Dylan J. Foster Nathan Srebro Blake Woodworth
+	The Complexity of Making the Gradient Small in Stochastic Convex Optimization	2019	Dylan J. Foster Ayush Sekhari Ohad Shamir Nathan Srebro Karthik Sridharan Blake Woodworth
+	Kernel and Rich Regimes in Overparametrized Models	2019	Blake Woodworth Suriya Gunasekar Pedro Savarese Edward Moroshko Itay Golan Jason Lee Daniel Soudry Nathan Srebro
+	Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization	2018	Blake Woodworth Jialei Wang Adam Smith Brendan McMahan Nati Srebro
+	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints	2018	Andrew Cotter Maya R. Gupta Heinrich Jiang Nathan Srebro Karthik Sridharan Serena Wang Blake Woodworth Seungil You
+ PDF Chat	Implicit Regularization in Matrix Factorization	2018	Suriya Gunasekar Blake Woodworth Srinadh Bhojanapalli Behnam Neyshabur Nathan Srebro
+	The Everlasting Database: Statistical Validity at a Fair Price	2018	Blake Woodworth Vitaly Feldman Saharon Rosset Nathan Srebro
+	Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization	2018	Blake Woodworth Jialei Wang Adam Smith Brendan McMahan Nathan Srebro
+	Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints	2018	Andrew Cotter Maya R. Gupta Heinrich Jiang Nathan Srebro Karthik Sridharan Serena Wang Blake Woodworth Seungil You
+	The Everlasting Database: Statistical Validity at a Fair Price	2018	Blake Woodworth Vitaly Feldman Saharon Rosset Nathan Srebro
+	Lower Bound for Randomized First Order Convex Optimization	2017	Blake Woodworth Nathan Srebro
+	Implicit Regularization in Matrix Factorization	2017	Suriya Gunasekar Blake Woodworth Srinadh Bhojanapalli Behnam Neyshabur Nathan Srebro
+	Learning Non-Discriminatory Predictors	2017	Blake Woodworth Suriya Gunasekar Mesrob I. Ohannessian Nathan Srebro
+	Learning Non-Discriminatory Predictors	2017	Blake Woodworth Suriya Gunasekar Mesrob I. Ohannessian Nathan Srebro
+	Implicit Regularization in Matrix Factorization	2017	Suriya Gunasekar Blake Woodworth Srinadh Bhojanapalli Behnam Neyshabur Nati Srebro
+	Lower Bound for Randomized First Order Convex Optimization	2017	Blake Woodworth Nathan Srebro
+	Implicit Regularization in Matrix Factorization	2017	Suriya Gunasekar Blake Woodworth Srinadh Bhojanapalli Behnam Neyshabur Nathan Srebro
+	Tight Complexity Bounds for Optimizing Composite Objectives	2016	Blake Woodworth Nathan Srebro
+	Tight Complexity Bounds for Optimizing Composite Objectives	2016	Blake Woodworth Nathan Srebro

Common Coauthors

Coauthor	Papers Together
Nathan Srebro	42
Suriya Gunasekar	15
Daniel Soudry	7
Ohad Shamir	7
Edward Moroshko	7
Kumar Kshitij Patel	6
Jason D. Lee	5
Brian Bullins	5
Francis Bach	4
Dylan J. Foster	4
Adam Smith	4
Behnam Neyshabur	4
Srinadh Bhojanapalli	4
Karthik Sridharan	4
Konstantin Mishchenko	3
Pedro Savarese	3
Nati Srebro	3
Itay Golan	3
Yair Carmon	2
Aaron Roth	2
Ryan Rogers	2
Max Simchowitz	2
Saharon Rosset	2
John C. Duchi	2
Serena Wang	2
Yossi Arjevani	2
Brendan McMahan	2
H. Brendan McMahan	2
Sebastian U. Stich	2
Om Thakkar	2
Heinrich Jiang	2
Maya R. Gupta	2
Mesrob I. Ohannessian	2
Mark Braverman	2
Elad Hazan	2
Andrew Cotter	2
Ayush Sekhari	2
Vitaly Feldman	2
Seungil You	2
Jialei Wang	2
Sanmi Koyejo	1
H. Jerry Qi	1
Jianyu Wang	1
Jason Lee	1
Mi Zhang	1
Hongyi Wang	1
Chen Zhu	1
Chunxiang Zheng	1
Luyang Liu	1
Zheng Xu	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Lower Bound for Randomized First Order Convex Optimization	2017	Blake Woodworth Nathan Srebro	6
+ PDF Chat	Lower bounds for finding stationary points I	2019	Yair Carmon John C. Duchi Oliver Hinder Aaron Sidford	6
+	Introductory Lectures on Convex Optimization: A Basic Course	2014	Ю Е Нестеров	5
+	Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms	2018	Jianyu Wang Gauri Joshi	5
+	Unified Optimal Analysis of the (Stochastic) Gradient Method	2019	Sebastian U. Stich	4
+	Local SGD Converges Fast and Communicates Little	2018	Sebastian U. Stich	4
+	Characterizing Implicit Bias in Terms of Optimization Geometry	2018	Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro	4
+	On Lazy Training in Differentiable Programming	2018	Lénaïc Chizat Edouard Oyallon Francis Bach	4
+	On the Convergence Properties of a K-step Averaging Stochastic Gradient Descent Algorithm for Nonconvex Optimization	2018	Fan Zhou Guojing Cong	4
+	Neural Tangent Kernel: Convergence and Generalization in Neural Networks	2018	Arthur Paul Jacot Franck Gabriel Clément Hongler	4
+	On the Randomized Complexity of Minimizing a Convex Quadratic Function	2018	Max Simchowitz	4
+	Optimization Methods for Large-Scale Machine Learning	2018	Léon Bottou Frank E. Curtis Jorge Nocedal	4
+	Parallel SGD: When does averaging help?	2016	Jian Zhang Christopher De Ioannis Mitliagkas Christopher Ré	4
+	In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning	2014	Behnam Neyshabur Ryota Tomioka Nathan Srebro	4
+	Better Communication Complexity for Local SGD	2019	Ahmed Khaled Konstantin Mishchenko Peter Richtárik	4
+	Minibatch vs Local SGD for Heterogeneous Distributed Learning	2020	Blake Woodworth Kumar Kshitij Patel Nathan Srebro	4
+	Parallelized Stochastic Gradient Descent	2010	Martin Zinkevich Markus Weimer Lihong Li Alex Smola	4
+	Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization	2018	Blake Woodworth Jialei Wang Adam Smith Brendan McMahan Nathan Srebro	3
+	Cubic regularization of Newton method and its global performance	2006	Yurii Nesterov B. T. Polyak	3
+	Federated Accelerated Stochastic Gradient Descent	2020	Honglin Yuan Tengyu Ma	3
+ PDF Chat	Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning	2019	Hao Yu Sen Yang Shenghuo Zhu	3
+	Divide and Conquer Kernel Ridge Regression	2013	Yuchen Zhang John C. Duchi Martin J. Wainwright	3
+	An Elementary Introduction to Modern Convex Geometry	1997	Keith Ball	3
+	Don't Use Large Mini-Batches, Use Local SGD	2018	Tao Lin Sebastian U. Stich Kumar Kshitij Patel Martin Jaggi	3
+	Implicit Regularization in Matrix Factorization	2017	Suriya Gunasekar Blake Woodworth Srinadh Bhojanapalli Behnam Neyshabur Nati Srebro	3
+	A Convergence Theory for Deep Learning via Over-Parameterization	2018	Zeyuan Allen-Zhu Yuanzhi Li Zhao Song	3
+	Tighter Theory for Local SGD on Identical and Heterogeneous Data	2020	Ahmed Khaled Konstantin Mishchenko Peter Richtárik	3
+	Information-theoretic lower bounds for distributed statistical estimation with communication constraints	2014	John C. Duchi Michael I. Jordan Martin J. Wainwright Yuchen Zhang	3
+	The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication.	2019	Sebastian U. Stich Sai Praneeth Karimireddy	3
+	Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization	2019	Farzin Haddadpour Mohammad Mahdi Kamani Mehrdad Mahdavi Viveck R. Cadambe	3
+ PDF Chat	Lower bounds for non-convex stochastic optimization	2022	Yossi Arjevani Yair Carmon John C. Duchi Dylan J. Foster Nathan Srebro Blake Woodworth	3
+	Algorithmic Regularization in Over-parameterized Matrix Sensing and Neural Networks with Quadratic Activations	2017	Yuanzhi Li Tengyu Ma Hongyang Zhang	3
+	Implicit Bias of Gradient Descent on Linear Convolutional Networks	2018	Suriya Gunasekar Jason D. Lee Daniel Soudry Nathan Srebro	3
+	Gradient Descent Provably Optimizes Over-parameterized Neural Networks	2018	Simon S. Du Xiyu Zhai Barnabás Póczos Aarti Singh	3
+	SCAFFOLD: Stochastic Controlled Averaging for Federated Learning	2019	Sai Praneeth Karimireddy Satyen Kale Mehryar Mohri Sashank J. Reddi Sebastian U. Stich Ananda Theertha Suresh	3
+	Convex Analysis and Monotone Operator Theory in Hilbert Spaces	2017	Heinz H. Bauschke Patrick L. Combettes	3
+ PDF Chat	Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming	2013	Saeed Ghadimi Guanghui Lan	3
+	SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator	2018	Cong Fang Chris Junchi Li Zhouchen Lin Tong Zhang	3
+	AIDE: Fast and Communication Efficient Distributed Optimization	2016	Sashank J. Reddi Jakub Konečný Peter Richtárik Barnabás Póczos Alexander J. Smola	3
+ PDF Chat	Communication lower bounds for statistical estimation problems via a distributed data processing inequality	2016	Mark Braverman Ankit Garg Tengyu Ma Huy L. Nguyễn David P. Woodruff	3
+	Geometry of Optimization and Implicit Regularization in Deep Learning	2017	Behnam Neyshabur Ryota Tomioka Ruslan Salakhutdinov Nathan Srebro	3
+	Non-convex Finite-Sum Optimization Via SCSG Methods	2017	Lihua Lei Cheng Ju Jianbo Chen Michael I. Jordan	3
+	DiSCO: Distributed Optimization for Self-Concordant Empirical Loss	2015	Yuchen Zhang Lin Xiao	3
+	Gradient Descent Maximizes the Margin of Homogeneous Neural Networks	2019	Kaifeng Lyu Jian Li	3
+	Information-theoretic lower bounds for distributed statistical estimation with communication constraints	2013	Yuchen Zhang John C. Duchi Michael I. Jordan Martin J. Wainwright	3
+ PDF Chat	Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization	2010	Benjamin Recht Maryam Fazel Pablo A. Parrilo	3
+	Is Local SGD Better than Minibatch SGD?	2020	Blake Woodworth Kumar Kshitij Patel Sebastian U. Stich Zhen Dai Brian Bullins H. Brendan McMahan Ohad Shamir Nathan Srebro	3
+	Gradient methods for minimizing composite functions	2012	Yu. Nesterov	2
+	On the Universality of Online Mirror Descent	2011	Nathan Srebro Karthik Sridharan Ambuj Tewari	2
+	Mirror descent and nonlinear projected subgradient methods for convex optimization	2003	Amir Beck Marc Teboulle	2