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Blake Woodworth
Blake Woodworth
Inria (SIERRA)
Verified email at inria.fr - Homepage
Title
Cited by
Cited by
Year
Implicit regularization in matrix factorization
S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro
Advances in Neural Information Processing Systems 30, 2017
3282017
Learning non-discriminatory predictors
B Woodworth, S Gunasekar, MI Ohannessian, N Srebro
Conference on Learning Theory, 1920-1953, 2017
3202017
Kernel and rich regimes in overparametrized models
B Woodworth, S Gunasekar, JD Lee, E Moroshko, P Savarese, I Golan, ...
Conference on Learning Theory, 3635-3673, 2020
1952020
Tight complexity bounds for optimizing composite objectives
BE Woodworth, N Srebro
Advances in neural information processing systems 29, 2016
1872016
Lower bounds for non-convex stochastic optimization
Y Arjevani, Y Carmon, JC Duchi, DJ Foster, N Srebro, B Woodworth
Mathematical Programming, 1-50, 2022
1632022
Is local SGD better than minibatch SGD?
B Woodworth, KK Patel, S Stich, Z Dai, B Bullins, B Mcmahan, O Shamir, ...
International Conference on Machine Learning, 10334-10343, 2020
1462020
A field guide to federated optimization
J Wang, Z Charles, Z Xu, G Joshi, HB McMahan, M Al-Shedivat, G Andrew, ...
arXiv preprint arXiv:2107.06917, 2021
1382021
Minibatch vs local sgd for heterogeneous distributed learning
BE Woodworth, KK Patel, N Srebro
Advances in Neural Information Processing Systems 33, 6281-6292, 2020
1072020
Graph oracle models, lower bounds, and gaps for parallel stochastic optimization
BE Woodworth, J Wang, A Smith, B McMahan, N Srebro
Advances in neural information processing systems 31, 2018
962018
Training well-generalizing classifiers for fairness metrics and other data-dependent constraints
A Cotter, M Gupta, H Jiang, N Srebro, K Sridharan, S Wang, B Woodworth, ...
International Conference on Machine Learning, 1397-1405, 2019
782019
Implicit bias in deep linear classification: Initialization scale vs training accuracy
E Moroshko, BE Woodworth, S Gunasekar, JD Lee, N Srebro, D Soudry
Advances in neural information processing systems 33, 22182-22193, 2020
522020
The complexity of making the gradient small in stochastic convex optimization
DJ Foster, A Sekhari, O Shamir, N Srebro, K Sridharan, B Woodworth
Conference on Learning Theory, 1319-1345, 2019
362019
On the implicit bias of initialization shape: Beyond infinitesimal mirror descent
S Azulay, E Moroshko, MS Nacson, BE Woodworth, N Srebro, ...
International Conference on Machine Learning, 468-477, 2021
302021
Lower bound for randomized first order convex optimization
B Woodworth, N Srebro
arXiv preprint arXiv:1709.03594, 2017
302017
The gradient complexity of linear regression
M Braverman, E Hazan, M Simchowitz, B Woodworth
Conference on Learning Theory, 627-647, 2020
222020
The min-max complexity of distributed stochastic convex optimization with intermittent communication
BE Woodworth, B Bullins, O Shamir, N Srebro
Conference on Learning Theory, 4386-4437, 2021
202021
Mirrorless mirror descent: A natural derivation of mirror descent
S Gunasekar, B Woodworth, N Srebro
International Conference on Artificial Intelligence and Statistics, 2305-2313, 2021
172021
Mirrorless mirror descent: a more natural discretization of riemannian gradient flow
S Gunasekar, B Woodworth, N Srebro
arXiv preprint arXiv:2004.01025, 2020
102020
An even more optimal stochastic optimization algorithm: minibatching and interpolation learning
BE Woodworth, N Srebro
Advances in Neural Information Processing Systems 34, 7333-7345, 2021
92021
Guaranteed validity for empirical approaches to adaptive data analysis
R Rogers, A Roth, A Smith, N Srebro, O Thakkar, B Woodworth
International Conference on Artificial Intelligence and Statistics, 2830-2840, 2020
92020
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