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Shivam Garg
Shivam Garg
Microsoft Research
Adresse e-mail validée de stanford.edu - Page d'accueil
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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
4502022
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
502016
Reward identification in inverse reinforcement learning
K Kim, S Garg, K Shiragur, S Ermon
International Conference on Machine Learning, 5496-5505, 2021
492021
A spectral view of adversarially robust features
S Garg, V Sharan, B Zhang, G Valiant
Advances in Neural Information Processing Systems 31, 2018
322018
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
152020
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
11*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
42022
On the statistical complexity of sample amplification
B Axelrod, S Garg, Y Han, V Sharan, G Valiant
The Annals of Statistics 52 (6), 2767-2790, 2024
32024
Attribute-to-delete: Machine unlearning via datamodel matching
K Georgiev, R Rinberg, SM Park, S Garg, A Ilyas, A Madry, S Neel
arXiv preprint arXiv:2410.23232, 2024
22024
Nature of Learning and Learning of Nature
S Garg
Stanford University, 2023
12023
Discovering Data Structures: Nearest Neighbor Search and Beyond
O Salemohamed, L Charlin, S Garg, V Sharan, G Valiant
arXiv preprint arXiv:2411.03253, 2024
2024
Testing with Non-identically Distributed Samples
S Garg, C Pabbaraju, K Shiragur, G Valiant
arXiv preprint arXiv:2311.11194, 2023
2023
Learning to Design Data-structures: A Case Study of Nearest Neighbor Search
O Salemohamed, V Sharan, S Garg, L Charlin, G Valiant
ICML 2024 Workshop on Differentiable Almost Everything: Differentiable …, 0
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