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Blake E. Woodworth
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2020 – today
- 2023
- [j1]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): 165-214 (2023) - [c25]Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach:
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. ICML 2023: 37273-37292 - [i24]Blake E. Woodworth, Konstantin Mishchenko, Francis R. Bach:
Two Losses Are Better Than One: Faster Optimization Using a Cheaper Proxy. CoRR abs/2302.03542 (2023) - 2022
- [c24]Blake E. Woodworth, Francis R. Bach, Alessandro Rudi:
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. COLT 2022: 4620-4642 - [c23]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: 5359-5363 - [c22]Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. NeurIPS 2022 - [c21]Kumar Kshitij Patel, Lingxiao Wang, Blake E. Woodworth, Brian Bullins, Nati Srebro:
Towards Optimal Communication Complexity in Distributed Non-Convex Optimization. NeurIPS 2022 - [i23]Blake E. Woodworth, Francis R. Bach, Alessandro Rudi:
Non-Convex Optimization with Certificates and Fast Rates Through Kernel Sums of Squares. CoRR abs/2204.04970 (2022) - [i22]Konstantin Mishchenko, Francis R. Bach, Mathieu Even, Blake E. Woodworth:
Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays. CoRR abs/2206.07638 (2022) - 2021
- [c20]Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro:
Mirrorless Mirror Descent: A Natural Derivation of Mirror Descent. AISTATS 2021: 2305-2313 - [c19]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. COLT 2021: 4386-4437 - [c18]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: 468-477 - [c17]Blake E. Woodworth, Nathan Srebro:
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. NeurIPS 2021: 7333-7345 - [c16]Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth:
A Stochastic Newton Algorithm for Distributed Convex Optimization. NeurIPS 2021: 26818-26830 - [i21]Blake E. Woodworth, Brian Bullins, Ohad Shamir, Nathan Srebro:
The Min-Max Complexity of Distributed Stochastic Convex Optimization with Intermittent Communication. CoRR abs/2102.01583 (2021) - [i20]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 abs/2102.09769 (2021) - [i19]Blake E. Woodworth, Nathan Srebro:
An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning. CoRR abs/2106.02720 (2021) - [i18]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
A Field Guide to Federated Optimization. CoRR abs/2107.06917 (2021) - [i17]Blake E. Woodworth:
The Minimax Complexity of Distributed Optimization. CoRR abs/2109.00534 (2021) - [i16]Brian Bullins, Kumar Kshitij Patel, Ohad Shamir, Nathan Srebro, Blake E. Woodworth:
A Stochastic Newton Algorithm for Distributed Convex Optimization. CoRR abs/2110.02954 (2021) - 2020
- [c15]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: 2830-2840 - [c14]Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth:
The Gradient Complexity of Linear Regression. COLT 2020: 627-647 - [c13]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: 3635-3673 - [c12]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: 10334-10343 - [c11]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 - [c10]Blake E. Woodworth, Kumar Kshitij Patel, Nati Srebro:
Minibatch vs Local SGD for Heterogeneous Distributed Learning. NeurIPS 2020 - [i15]Blake E. Woodworth, Kumar Kshitij Patel
Is Local SGD Better than Minibatch SGD? CoRR abs/2002.07839 (2020) - [i14]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 abs/2002.09277 (2020) - [i13]Suriya Gunasekar, Blake E. Woodworth, Nathan Srebro:
Mirrorless Mirror Descent: A More Natural Discretization of Riemannian Gradient Flow. CoRR abs/2004.01025 (2020) - [i12]Blake E. Woodworth, Kumar Kshitij Patel, Nathan Srebro:
Minibatch vs Local SGD for Heterogeneous Distributed Learning. CoRR abs/2006.04735 (2020) - [i11]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 abs/2007.06738 (2020)
2010 – 2019
- 2019
- [c9]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: 1319-1345 - [c8]Blake E. Woodworth, Nathan Srebro:
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. COLT 2019: 3202-3210 - [c7]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. ICML 2019: 1397-1405 - [i10]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 abs/1902.04686 (2019) - [i9]Ryan Rogers, Aaron Roth, Adam D. Smith, Nathan Srebro, Om Thakkar, Blake E. Woodworth:
Guaranteed Validity for Empirical Approaches to Adaptive Data Analysis. CoRR abs/1906.09231 (2019) - [i8]Blake E. Woodworth, Nathan Srebro:
Open Problem: The Oracle Complexity of Convex Optimization with Limited Memory. CoRR abs/1907.00762 (2019) - [i7]Mark Braverman, Elad Hazan, Max Simchowitz, Blake E. Woodworth:
The gradient complexity of linear regression. CoRR abs/1911.02212 (2019) - [i6]Yossi Arjevani, Yair Carmon, John C. Duchi, Dylan J. Foster, Nathan Srebro, Blake E. Woodworth:
Lower Bounds for Non-Convex Stochastic Optimization. CoRR abs/1912.02365 (2019) - 2018
- [c6]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Implicit Regularization in Matrix Factorization. ITA 2018: 1-10 - [c5]Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nati Srebro:
The Everlasting Database: Statistical Validity at a Fair Price. NeurIPS 2018: 6532-6541 - [c4]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: 8505-8515 - [i5]Blake E. Woodworth, Vitaly Feldman, Saharon Rosset, Nathan Srebro:
The Everlasting Database: Statistical Validity at a Fair Price. CoRR abs/1803.04307 (2018) - [i4]Blake E. Woodworth, Jialei Wang, Brendan McMahan, Nathan Srebro:
Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization. CoRR abs/1805.10222 (2018) - [i3]Andrew Cotter, Maya R. Gupta, Heinrich Jiang, Nathan Srebro, Karthik Sridharan, Serena Lutong Wang, Blake E. Woodworth, Seungil You:
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints. CoRR abs/1807.00028 (2018) - 2017
- [c3]Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro:
Learning Non-Discriminatory Predictors. COLT 2017: 1920-1953 - [c2]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nati Srebro:
Implicit Regularization in Matrix Factorization. NIPS 2017: 6151-6159 - [i2]Blake E. Woodworth, Suriya Gunasekar, Mesrob I. Ohannessian, Nathan Srebro:
Learning Non-Discriminatory Predictors. CoRR abs/1702.06081 (2017) - [i1]Suriya Gunasekar, Blake E. Woodworth, Srinadh Bhojanapalli, Behnam Neyshabur, Nathan Srebro:
Implicit Regularization in Matrix Factorization. CoRR abs/1705.09280 (2017) - 2016
- [c1]Blake E. Woodworth, Nati Srebro:
Tight Complexity Bounds for Optimizing Composite Objectives. NIPS 2016: 3639-3647
Coauthor Index
aka: Nati Srebro
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