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 | 73 | 2017 |
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 | 61 | 2021 |
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), 1035-1051, 2022 | 59 | 2022 |
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 | 47* | 2021 |
Statistical efficiency of score matching: The view from isoperimetry F Koehler, A Heckett, A Risteski arXiv preprint arXiv:2210.00726, 2022 | 43 | 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 | 41 | 2019 |
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 | 36 | 2016 |
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 | 33 | 2022 |
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 | 32 | 2019 |
The mean-field approximation: Information inequalities, algorithms, and complexity V Jain, F Koehler, E Mossel Conference On Learning Theory, 1326-1347, 2018 | 32 | 2018 |
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 | 31 | 2020 |
Entropic independence: optimal mixing of down-up random walks N Anari, V Jain, F Koehler, HT Pham, TD Vuong Proceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing …, 2022 | 30 | 2022 |
Representational aspects of depth and conditioning in normalizing flows F Koehler, V Mehta, A Risteski International Conference on Machine Learning, 5628-5636, 2021 | 29 | 2021 |
Optimal batch schedules for parallel machines F Koehler, S Khuller Algorithms and Data Structures: 13th International Symposium, WADS 2013 …, 2013 | 29 | 2013 |
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 | 27 | 2020 |
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 | 26* | 2019 |
Optimistic rates: A unifying theory for interpolation learning and regularization in linear regression L Zhou, F Koehler, DJ Sutherland, N Srebro ACM/JMS Journal of Data Science 1 (2), 1-51, 2024 | 24 | 2024 |
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 | 21 | 2022 |
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 | 21 | 2021 |
A non-asymptotic moreau envelope theory for high-dimensional generalized linear models L Zhou, F Koehler, P Sur, DJ Sutherland, N Srebro Advances in Neural Information Processing Systems 35, 21286-21299, 2022 | 19 | 2022 |