Blake E. Woodworth

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 13 papers; trusts 75 researchers and is trusted by 53 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 319 citations into it; #5,062 of 35,345 by trust here, #5,924 by citations
• Optimization & Training — 175 citations into it; #699 of 2,601 by trust here, #738 by citations
• Computational Mechanics — 106 citations into it; #992 of 10,001 by trust here, #937 by citations
• Federated & Distributed Learning — 75 citations into it
• Computer Vision and Pattern Recognition — 53 citations into it; #14,647 of 21,575 by trust here, #12,419 by citations
• Computer Networks and Communications — 37 citations into it
• Computational Theory and Mathematics — 32 citations into it; #3,419 of 12,182 by trust here, #3,448 by citations
• Management Science and Operations Research — 31 citations into it

Whom Blake E. Woodworth trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yurii E. Nesterov
2. Nati Srebro
3. Shai Shalev-Shwartz
4. Tong Zhang
5. Ohad Shamir
6. John C. Duchi
7. Guanghui Lan
8. Saeed Ghadimi
9. Sebastian U. Stich
10. Zeyuan Allen-Zhu

Who trusts Blake E. Woodworth overall

1. Zeyuan Allen-Zhu
2. Nati Srebro
3. Yair Carmon
4. Andrew Cotter
5. Hubert Eichner
6. H. Brendan McMahan
7. Luca Oneto
8. Maruan Al-Shedivat
9. Cong Fang
10. Heinrich Jiang

Papers from Blake E. Woodworth's perspective

Ordered by how strongly the researchers Blake E. Woodworth trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Learning Multiple Layers of Features from Tiny Images
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Graph Oracle Models, Lower Bounds, and Gaps for Parallel Stochastic Optimization (2018) — 128 citations
• Kernel and Rich Regimes in Overparametrized Models (2019) — 437 citations
• Tight Complexity Bounds for Optimizing Composite Objectives (2016) — 199 citations
• Lower Bound for Randomized First Order Convex Optimization (2017) — 29 citations
• The Complexity of Making the Gradient Small in Stochastic Convex Optimization (2019) — 60 citations
• Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints (2018) — 119 citations
• Lower bounds for non-convex stochastic optimization (2019) — 476 citations
• Is Local SGD Better than Minibatch SGD? (2020) — 290 citations
• A Field Guide to Federated Optimization (2021) — 487 citations
• Minibatch vs Local SGD for Heterogeneous Distributed Learning (2020) — 228 citations
• Kernel and Deep Regimes in Overparametrized Models (2019) — 33 citations
• Implicit Regularization in Matrix Factorization (2017) — 585 citations
• Learning Non-Discriminatory Predictors (2017) — 404 citations