| Settling the robust learnability of mixtures of gaussians A Liu, A Moitra Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 57 | 2021 |
| When does adaptivity help for quantum state learning? S Chen, B Huang, J Li, A Liu, M Sellke 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS …, 2023 | 50* | 2023 |
| Tensor completion made practical A Liu, A Moitra Advances in Neural Information Processing Systems 33, 18905-18916, 2020 | 44 | 2020 |
| Efficiently learning mixtures of mallows models A Liu, A Moitra 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS …, 2018 | 43 | 2018 |
| Optimal contextual pricing and extensions A Liu, RP Leme, J Schneider Proceedings of the 2021 ACM-SIAM Symposium on Discrete Algorithms (SODA …, 2021 | 35 | 2021 |
| Variable decomposition for prophet inequalities and optimal ordering A Liu, RP Leme, M Pál, J Schneider, B Sivan arXiv preprint arXiv:2004.10163, 2020 | 35 | 2020 |
| Learning quantum Hamiltonians at any temperature in polynomial time A Bakshi, A Liu, A Moitra, E Tang Proceedings of the 56th Annual ACM Symposium on Theory of Computing, 1470-1477, 2024 | 32* | 2024 |
| Fourier and circulant matrices are not rigid Z Dvir, A Liu arXiv preprint arXiv:1902.07334, 2019 | 29 | 2019 |
| Tight bounds for quantum state certification with incoherent measurements S Chen, J Li, B Huang, A Liu 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 28 | 2022 |
| Clustering mixtures with almost optimal separation in polynomial time A Liu, J Li Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing …, 2022 | 26 | 2022 |
| Minimax rates for robust community detection A Liu, A Moitra 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 22 | 2022 |
| High-temperature Gibbs states are unentangled and efficiently preparable A Bakshi, A Liu, A Moitra, E Tang arXiv preprint arXiv:2403.16850, 2024 | 18 | 2024 |
| Learning gmms with nearly optimal robustness guarantees A Liu, A Moitra Conference on Learning Theory, 2815-2895, 2022 | 18 | 2022 |
| A new approach to learning linear dynamical systems A Bakshi, A Liu, A Moitra, M Yau Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 335-348, 2023 | 17 | 2023 |
| Semi-random sparse recovery in nearly-linear time J Kelner, J Li, AX Liu, A Sidford, K Tian The Thirty Sixth Annual Conference on Learning Theory, 2352-2398, 2023 | 15 | 2023 |
| Better algorithms for estimating non-parametric models in crowd-sourcing and rank aggregation A Liu, A Moitra Conference on Learning Theory, 2780-2829, 2020 | 13 | 2020 |
| Structure learning of Hamiltonians from real-time evolution A Bakshi, A Liu, A Moitra, E Tang arXiv preprint arXiv:2405.00082, 2024 | 12* | 2024 |
| Estimates for Bilinear Generalized Radon Transforms in the Plane A Greenleaf, A Iosevich, B Krause, A Liu Combinatorial and Additive Number Theory, New York Number Theory Seminar …, 2021 | 9* | 2021 |
| Robust model selection and nearly-proper learning for GMMs A Liu, J Li, A Moitra Advances in Neural Information Processing Systems 35, 22830-22843, 2022 | 8* | 2022 |
| Tensor decompositions meet control theory: learning general mixtures of linear dynamical systems A Bakshi, A Liu, A Moitra, M Yau International Conference on Machine Learning, 1549-1563, 2023 | 7 | 2023 |
Ankur MoitraNorbert Wiener Professor of Mathematics, MITVerified email at mit.edu
