| Information theoretic properties of Markov random fields, and their algorithmic applications L Hamilton, F Koehler, A Moitra Advances in Neural Information Processing Systems 30, 2017 | 58 | 2017 |
| Provable algorithms for inference in topic models S Arora, R Ge, F Koehler, T Ma, A Moitra International Conference on Machine Learning, 2859-2867, 2016 | 34 | 2016 |
| Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting F Koehler, L Zhou, D Sutherland, N Srebro Advances in Neural Information Processing Systems 34, 20657-20668, 2021 | 29 | 2021 |
| Entropic independence I: Modified log-Sobolev inequalities for fractionally log-concave distributions and high-temperature ising models N Anari, V Jain, F Koehler, HT Pham, TD Vuong arXiv preprint arXiv:2106.04105, 2021 | 28* | 2021 |
| Optimal batch schedules for parallel machines F Koehler, S Khuller Algorithms and Data Structures: 13th International Symposium, WADS 2013 …, 2013 | 28 | 2013 |
| A spectral condition for spectral gap: fast mixing in high-temperature Ising models R Eldan, F Koehler, O Zeitouni Probability theory and related fields 182 (3-4), 1035-1051, 2022 | 25 | 2022 |
| Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective V Jain, F Koehler, A Risteski Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 24 | 2019 |
| Learning restricted Boltzmann machines via influence maximization G Bresler, F Koehler, A Moitra Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 24 | 2019 |
| The comparative power of relu networks and polynomial kernels in the presence of sparse latent structure F Koehler, A Risteski International Conference on Learning Representations, 2019 | 21* | 2019 |
| The mean-field approximation: Information inequalities, algorithms, and complexity V Jain, F Koehler, E Mossel Conference On Learning Theory, 1326-1347, 2018 | 21 | 2018 |
| Online and distribution-free robustness: Regression and contextual bandits with huber contamination S Chen, F Koehler, A Moitra, M Yau 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 20 | 2022 |
| Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability S Chen, F Koehler, A Moitra, M Yau Advances in Neural Information Processing Systems 33, 2020 | 20 | 2020 |
| Learning some popular gaussian graphical models without condition number bounds J Kelner, F Koehler, R Meka, A Moitra Advances in Neural Information Processing Systems 33, 10986-10998, 2020 | 19 | 2020 |
| How many subpopulations is too many? Exponential lower bounds for inferring population histories Y Kim, F Koehler, A Moitra, E Mossel, G Ramnarayan Research in Computational Molecular Biology: 23rd Annual International …, 2019 | 14 | 2019 |
| Busy time scheduling on a bounded number of machines F Koehler, S Khuller Algorithms and Data Structures: 15th International Symposium, WADS 2017, St …, 2017 | 14 | 2017 |
| Fast convergence of belief propagation to global optima: Beyond correlation decay F Koehler Advances in Neural Information Processing Systems 32, 2019 | 13 | 2019 |
| Representational aspects of depth and conditioning in normalizing flows F Koehler, V Mehta, A Risteski International Conference on Machine Learning, 5628-5636, 2021 | 10 | 2021 |
| Entropic independence ii: optimal sampling and concentration via restricted modified log-Sobolev inequalities N Anari, V Jain, F Koehler, HT Pham, TD Vuong arXiv preprint arXiv:2111.03247, 2021 | 9 | 2021 |
| On the power of preconditioning in sparse linear regression JA Kelner, F Koehler, R Meka, D Rohatgi 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 8 | 2022 |
| Optimistic rates: A unifying theory for interpolation learning and regularization in linear regression L Zhou, F Koehler, DJ Sutherland, N Srebro arXiv preprint arXiv:2112.04470, 2021 | 6 | 2021 |
