Sanjeev Arora

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6 Similar Profiles

2 Active
5 Finished

Going beyond a black-box optimization view of deep learning
Arora, S.
Office of Naval Research - MA
4/7/20 → 4/6/23
Project: Research project
AF: Large: Collaborative Research: Nonconvex methods and models for learning: Toward algorithms with provable and interpretable guarantees
Arora, S.
NSF - National Science Foundation
6/1/17 → 5/31/22
Project: Research project
Unsupervised Learning with Provable Guarantees
Arora, S.
Office of Naval Research - MA
4/1/16 → 3/31/20
Project: Research project
AF: Small: Linear Algebra++ and Applications to Machine Learning
Arora, S.
NSF - National Science Foundation
6/15/15 → 5/31/19
Project: Research project
AF: Medium: Towards Provable Bounds for Machine Learning
Arora, S.
NSF - National Science Foundation
9/1/13 → 8/31/17
Project: Research project

7399 Citations
40 h-Index
43 Conference contribution
35 Article
17 Conference article
11 Paper
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Why don't today's deep nets overfit to their training data?
Arora, S., Mar 2021, In: Communications of the ACM. 64, 3, p. 106 1 p.
Research output: Contribution to journal › Comment/debate › peer-review

Open Access
A sample complexity separation between non-convex and convex meta-learning
Saunshi, N., Zhang, Y., Khodak, M. & Arora, S., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 8470-8479 10 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-11).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
InstaHide: Instance-hiding schemes for private distributed learning
Huang, Y., Song, Z., Li, K. & Arora, S., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 4457-4468 12 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-6).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Provable representation learning for imitation learning via bi-level optimization
Arora, S., Du, S. S., Kakade, S., Luo, Y. & Saunshi, N., 2020, 37th International Conference on Machine Learning, ICML 2020. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), p. 344-353 10 p. (37th International Conference on Machine Learning, ICML 2020; vol. PartF168147-1).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
A convergence analysis of gradient descent for deep linear neural networks
Arora, S., Golowich, N., Cohen, N. & Hu, W., Jan 1 2019.
Research output: Contribution to conference › Paper › peer-review

Sanjeev Arora

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Going beyond a black-box optimization view of deep learning

AF: Large: Collaborative Research: Nonconvex methods and models for learning: Toward algorithms with provable and interpretable guarantees

Unsupervised Learning with Provable Guarantees

AF: Small: Linear Algebra++ and Applications to Machine Learning

AF: Medium: Towards Provable Bounds for Machine Learning

Why don't today's deep nets overfit to their training data?

A sample complexity separation between non-convex and convex meta-learning

InstaHide: Instance-hiding schemes for private distributed learning

Provable representation learning for imitation learning via bi-level optimization

A convergence analysis of gradient descent for deep linear neural networks

Sanjeev Arora

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