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Sanjeev Arora (born January 1968) is an Indian American theoretical computer scientist who works in AI and Machine learning.
Sanjeev Arora. Research Interest: Developing mathematical and conceptual understanding to enable better and safer AI. Charles C. Fitzmorris Professor of Computer Science Director, Princeton Language and Intelligence PhD, UC Berkeley 1994
Sanjeev Arora. Professor of Computer Science, Princeton University. Verified email at cs.princeton.edu - Homepage. theoretical machine learning theoretical computer science. Articles Cited by Public access Co-authors. Title. ... S Arora, SS Du, W Hu, Z Li, R Salakhutdinov, R Wang. Neurips 2019 arXiv preprint arXiv:1904.11955, 2019. 920: 2019: Generalization and equilibrium in generative adversarial nets (gans)
Sanjeev Arora is the Charles C. Fitzmorris Professor in Computer Science. He joined Princeton in 1994 after earning his PhD from the UC Berkeley.
Education. SB in Math with Computer Science, MIT, 1990. (Also attended IIT Kanpur in India 1986-88 before transfering.) PhD in Computer Science, UC Berkeley, 1994. Dissertation topic: Probabilistic checking of proofs and the hardness of approximation problems. Advisor: Umesh Vazirani.
The goal of our group is to provide algorithms with such performance guarantees. This can involve designing fundamentally new algorithms, or proving such guarantees for existing algorithms. A lot of our work has been devoted to obtaining better mathematical understanding of deep learning.
Sanjeev Arora Charles C. Fitzmorris Professor in Computer Science. Website: http://unsupervised.cs.princeton.edu/ Email: arora@cs.Princeton.EDU. Office: Computer Science Building, 307. Phone: 609-258-3869. Research interests: Theoretical computer science and theoretical machine learning. Computational complexity (especially probabilistically ...
Sanjeev Arora is Charles C. Fitzmorris Professor of Computer Science at Princeton University. He got his PhD at UC Berkeley in 1994. His research area spans several areas of theoretical Computer Science, including computational complexity and algorithm design, and theoretical problems in machine learning.
Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets. NeurIPS 2019 (Rohith Kuditipudi, Xiang Wang, Holden Lee, Yi Zhang, Zhiyuan Li, Wei Hu, Sanjeev Arora, Rong Ge) Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle.
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