| 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 | 82 | 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 | 77 | 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 | 74 | 2022 |
| Statistical efficiency of score matching: The view from isoperimetry F Koehler, A Heckett, A Risteski arXiv preprint arXiv:2210.00726, 2022 | 60 | 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 | 56* | 2021 |
| 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 | 44 | 2019 |
| 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 | 43 | 2022 |
| Representational aspects of depth and conditioning in normalizing flows F Koehler, V Mehta, A Risteski International Conference on Machine Learning, 5628-5636, 2021 | 38 | 2021 |
| 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 | 37 | 2020 |
| 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 | 36 | 2022 |
| 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 |
| The mean-field approximation: Information inequalities, algorithms, and complexity V Jain, F Koehler, E Mossel Conference on learning theory, 1326-1347, 2018 | 35 | 2018 |
| 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 | 34 | 2019 |
| 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 | 29 | 2020 |
| Optimal batch schedules for parallel machines F Koehler, S Khuller Algorithms and Data Structures: 13th International Symposium, WADS 2013 …, 2013 | 28 | 2013 |
| 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, 2018 | 27* | 2018 |
| 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 | 26 | 2024 |
| 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 | 25 | 2022 |
| Sampling approximately low-rank Ising models: MCMC meets variational methods F Koehler, H Lee, A Risteski Conference on Learning Theory, 4945-4988, 2022 | 24 | 2022 |
| 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 | 23 | 2022 |
Vishesh JainUniversity of Illinois at ChicagoVerificeret mail på uic.edu
