Shivam Garg

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Gregory ValiantAssistant Professor of Computer Science, Stanford UniversityVerified email at stanford.edu
Kirankumar ShiragurPostdoctoral researcherVerified email at stanford.edu
Vatsal SharanStanford UniversityVerified email at stanford.edu
Percy LiangAssociate Professor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Brian AxelrodStanfordVerified email at cs.stanford.edu
Geevarghese PhilipFaculty Member at Chennai Mathematical InstituteVerified email at cmi.ac.in
Stefano ErmonStanford UniversityVerified email at cs.stanford.edu
Kuno KimPh.D Candidate, Stanford UniversityVerified email at cs.stanford.edu
Brian Hu ZhangComputer Science Department, Carnegie Mellon UniversityVerified email at cs.cmu.edu
Moses CharikarProfessor of Computer Science, Stanford UniversityVerified email at cs.stanford.edu
Deborah M GordonProfessor, Dept. of Biology, Stanford UniversityVerified email at stanford.edu
Santosh S. VempalaGeorgia TechVerified email at gatech.edu
Yanjun HanAssistant Professor, New York UniversityVerified email at nyu.edu
Dimitris TsiprasOpenAI

Shivam Garg

Stanford University

Verified email at stanford.edu - Homepage


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
What can transformers learn in-context? a case study of simple function classes S Garg, D Tsipras, PS Liang, G Valiant Advances in Neural Information Processing Systems 35, 30583-30598, 2022	239	2022
Raising The Bar For Vertex Cover: Fixed-parameter Tractability Above A Higher Guarantee S Garg, G Philip Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete …, 2016	47	2016
Reward identification in inverse reinforcement learning K Kim, S Garg, K Shiragur, S Ermon International Conference on Machine Learning, 5496-5505, 2021	31	2021
A spectral view of adversarially robust features S Garg, V Sharan, B Zhang, G Valiant Advances in Neural Information Processing Systems 31, 2018	29	2018
Sample Amplification: Increasing Dataset Size even when Learning is Impossible B Axelrod, S Garg, V Sharan, G Valiant International Conference on Machine Learning, 442-451, 2020	14	2020
Distributed algorithms from arboreal ants for the shortest path problem S Garg, K Shiragur, DM Gordon, M Charikar Proceedings of the National Academy of Sciences 120 (6), e2207959120, 2023	5*	2023
How and when random feedback works: A case study of low-rank matrix factorization S Garg, S Vempala International Conference on Artificial Intelligence and Statistics, 4070-4108, 2022	4	2022
On the statistical complexity of sample amplification B Axelrod, S Garg, Y Han, V Sharan, G Valiant arXiv preprint arXiv:2201.04315, 2022	3	2022
Testing with Non-identically Distributed Samples S Garg, C Pabbaraju, K Shiragur, G Valiant arXiv preprint arXiv:2311.11194, 2023		2023
Nature of Learning and Learning of Nature S Garg Stanford University, 2023		2023

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