| Textbooks are all you need S Gunasekar, Y Zhang, J Aneja, CCT Mendes, A Del Giorno, S Gopi, ... arXiv preprint arXiv:2306.11644, 2023 | 575 | 2023 |
| Differentially private fine-tuning of language models D Yu, S Naik, A Backurs, S Gopi, HA Inan, G Kamath, J Kulkarni, YT Lee, ... International Conference on Learning Representations (ICLR), 2022 | 360 | 2022 |
| Phi-2: The surprising power of small language models M Javaheripi, S Bubeck, M Abdin, J Aneja, S Bubeck, CCT Mendes, ... Microsoft Research Blog 1 (3), 3, 2023 | 216 | 2023 |
| 2-server PIR with subpolynomial communication Z Dvir, S Gopi Journal of the ACM (JACM) 63 (4), 1-15, 2016 | 195 | 2016 |
| Numerical composition of differential privacy S Gopi, YT Lee, L Wutschitz Advances in Neural Information Processing Systems 34, 11631-11642, 2021 | 177 | 2021 |
| One-bit compressed sensing: Provable support and vector recovery S Gopi, P Netrapalli, P Jain, A Nori International Conference on Machine Learning, 154-162, 2013 | 99 | 2013 |
| Trellis BMA: Coded trace reconstruction on IDS channels for DNA storage SR Srinivasavaradhan, S Gopi, HD Pfister, S Yekhanin 2021 IEEE International Symposium on Information Theory (ISIT), 2453-2458, 2021 | 67 | 2021 |
| Private convex optimization via exponential mechanism S Gopi, YT Lee, D Liu Conference on Learning Theory, 1948-1989, 2022 | 64 | 2022 |
| Privacy-preserving in-context learning with differentially private few-shot generation X Tang, R Shin, HA Inan, A Manoel, F Mireshghallah, Z Lin, S Gopi, ... International Conference on Learning Representations (ICLR), 2024 | 53 | 2024 |
| Fast and memory efficient differentially private-sgd via jl projections Z Bu, S Gopi, J Kulkarni, YT Lee, H Shen, U Tantipongpipat Advances in Neural Information Processing Systems 34, 19680-19691, 2021 | 47 | 2021 |
| Maximally recoverable LRCs: A field size lower bound and constructions for few heavy parities S Gopi, V Guruswami, S Yekhanin IEEE Transactions on Information Theory 66 (10), 6066-6083, 2020 | 45 | 2020 |
| Locally testable and locally correctable codes approaching the Gilbert-Varshamov bound S Gopi, S Kopparty, R Oliveira, N Ron-Zewi, S Saraf IEEE Transactions on Information Theory 64 (8), 5813-5831, 2018 | 42 | 2018 |
| Competitive analysis of the top-K ranking problem X Chen, S Gopi, J Mao, J Schneider Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete …, 2017 | 42 | 2017 |
| Differentially private set union S Gopi, P Gulhane, J Kulkarni, JH Shen, M Shokouhi, S Yekhanin International Conference on Machine Learning, 3627-3636, 2020 | 41 | 2020 |
| Locally private hypothesis selection S Gopi, G Kamath, J Kulkarni, A Nikolov, ZS Wu, H Zhang Conference on Learning Theory, 1785-1816, 2020 | 38 | 2020 |
| Differentially private synthetic data via foundation model apis 1: Images Z Lin, S Gopi, J Kulkarni, H Nori, S Yekhanin International Conference on Learning Representations (ICLR), 2024 | 35 | 2024 |
| Generic reed-solomon codes achieve list-decoding capacity J Brakensiek, S Gopi, V Makam Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 1488-1501, 2023 | 34 | 2023 |
| Improved maximally recoverable LRCs using skew polynomials S Gopi, V Guruswami IEEE Transactions on Information Theory 68 (11), 7198-7214, 2022 | 28 | 2022 |
| Selective pre-training for private fine-tuning D Yu, S Gopi, J Kulkarni, Z Lin, S Naik, TL Religa, J Yin, H Zhang Transactions on Machine Learning Research (TMLR), 2024 | 26 | 2024 |
| Differentially private synthetic data via foundation model apis 2: Text C Xie, Z Lin, A Backurs, S Gopi, D Yu, HA Inan, H Nori, H Jiang, H Zhang, ... International Conference on Machine Learning (ICML), 2024 | 23 | 2024 |
Janardhan KulkarniMicrosoft Research, RedmondEmail được xác minh tại cs.washington.edu