| Superpolynomial lower bounds for learning one-layer neural networks using gradient descent S Goel, A Gollakota, Z Jin, S Karmalkar, A Klivans International Conference on Machine Learning, 3587-3596, 2020 | 85 | 2020 |
| Statistical-query lower bounds via functional gradients S Goel, A Gollakota, A Klivans Advances in Neural Information Processing Systems 33, 2147-2158, 2020 | 68 | 2020 |
| Ambient diffusion: Learning clean distributions from corrupted data G Daras, K Shah, Y Dagan, A Gollakota, A Dimakis, A Klivans Advances in Neural Information Processing Systems 36, 288-313, 2023 | 53 | 2023 |
| Hardness of noise-free learning for two-hidden-layer neural networks S Chen, A Gollakota, A Klivans, R Meka Advances in Neural Information Processing Systems 35, 10709-10724, 2022 | 37 | 2022 |
| On the hardness of PAC-learning stabilizer states with noise A Gollakota, D Liang Quantum 6, 640, 2022 | 20 | 2022 |
| A moment-matching approach to testable learning and a new characterization of rademacher complexity A Gollakota, AR Klivans, PK Kothari Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 1657-1670, 2023 | 18 | 2023 |
| Agnostically learning single-index models using omnipredictors A Gollakota, P Gopalan, A Klivans, K Stavropoulos Advances in Neural Information Processing Systems 36, 14685-14704, 2023 | 15 | 2023 |
| Tester-learners for halfspaces: Universal algorithms A Gollakota, A Klivans, K Stavropoulos, A Vasilyan Advances in Neural Information Processing Systems 36, 10145-10169, 2023 | 15 | 2023 |
| An efficient tester-learner for halfspaces A Gollakota, AR Klivans, K Stavropoulos, A Vasilyan arXiv preprint arXiv:2302.14853, 2023 | 13 | 2023 |
| Packing tree degree sequences A Gollakota, W Hardt, I Miklós Graphs and Combinatorics 36 (3), 779-801, 2020 | 3 | 2020 |
| Provable Uncertainty Decomposition via Higher-Order Calibration G Ahdritz, A Gollakota, P Gopalan, C Peale, U Wieder arXiv preprint arXiv:2412.18808, 2024 | 1 | 2024 |
| The polynomial method is universal for distribution-free correlational SQ learning A Gollakota, S Karmalkar, A Klivans arXiv preprint arXiv:2010.11925, 2020 | 1 | 2020 |
| When does a predictor know its own loss? A Gollakota, P Gopalan, A Karan, C Peale, U Wieder arXiv preprint arXiv:2502.20375, 2025 | | 2025 |
| New computational and statistical characterizations of neural network learning A Gollakota | | 2023 |
Adam KlivansProfessor of Computer Science, University of Texas at Austin在 cs.utexas.edu 的电子邮件经过验证
