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Frederic Koehler
Frederic Koehler
Verified email at stanford.edu - Homepage
Title
Cited by
Cited by
Year
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
552017
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
312016
Optimal batch schedules for parallel machines
F Koehler, S Khuller
Workshop on Algorithms and Data Structures, 475-486, 2013
302013
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 e-prints, arXiv: 2106.04105, 2021
27*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
232022
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
232021
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
212019
The mean-field approximation: Information inequalities, algorithms, and complexity
V Jain, F Koehler, E Mossel
Conference On Learning Theory, 1326-1347, 2018
212018
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
202020
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
202019
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
20*2018
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
162020
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
152022
How many subpopulations is too many? Exponential lower bounds for inferring population histories
Y Kim, F Koehler, A Moitra, E Mossel, G Ramnarayan
International Conference on Research in Computational Molecular Biology, 136-157, 2019
142019
Fast convergence of belief propagation to global optima: Beyond correlation decay
F Koehler
Advances in Neural Information Processing Systems 32, 2019
122019
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
112021
Busy time scheduling on a bounded number of machines
F Koehler, S Khuller
Workshop on Algorithms and Data Structures, 521-532, 2017
112017
Representational aspects of depth and conditioning in normalizing flows
F Koehler, V Mehta, A Risteski
International Conference on Machine Learning, 5628-5636, 2021
102021
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
62022
The vertex sample complexity of free energy is polynomial
V Jain, F Koehler, E Mossel
Conference On Learning Theory, 1395-1419, 2018
62018
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