| Testing distributional assumptions of learning algorithms R Rubinfeld, A Vasilyan Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 1643-1656, 2023 | 14 | 2023 |
| Properly learning monotone functions via local correction J Lange, R Rubinfeld, A Vasilyan 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS), 75-86, 2022 | 8* | 2022 |
| An efficient tester-learner for halfspaces A Gollakota, AR Klivans, K Stavropoulos, A Vasilyan arXiv preprint arXiv:2302.14853, 2023 | 6 | 2023 |
| Tester-Learners for Halfspaces: Universal Algorithms A Gollakota, AR Klivans, K Stavropoulos, A Vasilyan Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 5 | 2023 |
| Agnostic proper learning of monotone functions: beyond the black-box correction barrier J Lange, A Vasilyan IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS), 1149-1170, 2023 | 4 | 2023 |
| Approximating the noise sensitivity of a monotone boolean function R Rubinfeld, A Vasilyan Approximation, Randomization, and Combinatorial Optimization. Algorithms and …, 2019 | 4 | 2019 |
| Testable Learning with Distribution Shift AR Klivans, K Stavropoulos, A Vasilyan arXiv preprint arXiv:2311.15142, 2023 | 2 | 2023 |
| Monotone probability distributions over the Boolean cube can be learned with sublinear samples R Rubinfeld, A Vasilyan 11th Innovations in Theoretical Computer Science Conference, ITCS 151, 28:1 …, 2020 | 2 | 2020 |
| Local Lipschitz Filters for Bounded-Range Functions with Applications to Arbitrary Real-Valued Functions J Lange, E Linder, S Raskhodnikova, A Vasilyan arXiv e-prints, arXiv: 2308.14716, 2023 | 1* | 2023 |
| Learning Intersections of Halfspaces with Distribution Shift: Improved Algorithms and SQ Lower Bounds AR Klivans, K Stavropoulos, A Vasilyan arXiv preprint arXiv:2404.02364, 2024 | | 2024 |
| Approximating the noise sensitivity of a monotone Boolean function A Vasilyan Massachusetts Institute of Technology, 2020 | | 2020 |
