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Dive into the research topics where Sanjeev Arora is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborative Research:RI:Medium:MoDL:Mathematical and Conceptual Understanding of Large Language Models
NSF - National Science Foundation
10/1/22 → 9/30/25
Project: Research project
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AF: Small: Linear Algebra++ and Applications to Machine Learning
NSF - National Science Foundation
6/15/15 → 5/31/19
Project: Research project
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Evaluating Gradient Inversion Attacks and Defenses in Federated Learning
Huang, Y., Gupta, S., Song, Z., Li, K. & Arora, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 7232-7241 10 p. (Advances in Neural Information Processing Systems; vol. 9).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
8 Scopus citations -
Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias
Lyu, K., Li, Z., Wang, R. & Arora, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 12978-12991 14 p. (Advances in Neural Information Processing Systems; vol. 16).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
1 Scopus citations -
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)
Li, Z., Malladi, S. & Arora, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 12712-12725 14 p. (Advances in Neural Information Processing Systems; vol. 16).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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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
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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
2 Scopus citations