Frederic Koehler

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Ankur MoitraNorbert Wiener Professor of Mathematics, MITDirección de correo verificada de mit.edu
Vishesh JainUniversity of Illinois at ChicagoDirección de correo verificada de uic.edu
Andrej RisteskiCarnegie Mellon UniversityDirección de correo verificada de andrew.cmu.edu
Thuy-Duong VuongPhD Student in Computer Science, Stanford UniversityDirección de correo verificada de stanford.edu
Elchanan MosselProfessor of Mathematics, MITDirección de correo verificada de mit.edu
Nima AnariStanford UniversityDirección de correo verificada de cs.stanford.edu
Nathan SrebroProfessor, TTIC and University of ChicagoDirección de correo verificada de ttic.edu
Huy Tuan PhamStanford UniversityDirección de correo verificada de stanford.edu
Danica J. SutherlandUniversity of British Columbia + AmiiDirección de correo verificada de cs.ubc.ca
Raghu MekaUniversity of California, Los AngelesDirección de correo verificada de cs.ucla.edu
Morris YauMITDirección de correo verificada de mit.edu
Sitan ChenAssistant Professor of Computer Science, Harvard UniversityDirección de correo verificada de seas.harvard.edu
Dhruv RohatgiMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
samir khullerBarris Professor and Chair of Computer Science, Northwestern UniversityDirección de correo verificada de northwestern.edu
Guy BreslerMITDirección de correo verificada de mit.edu
Viraj MehtaTensorZeroDirección de correo verificada de tensorzero.com
Holden LeeAssistant Professor of Applied Mathematics and Statistics, Johns Hopkins UniversityDirección de correo verificada de jhu.edu
Enric Boix-AdseràPhD Student in EECS, MITDirección de correo verificada de mit.edu
Jayson LynchMITDirección de correo verificada de mit.edu
Adam HesterbergHarvard SEASDirección de correo verificada de seas.harvard.edu

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Frederic Koehler

University of Chicago

Dirección de correo verificada de uchicago.edu - Página principal

Theoretical Computer Science Machine Learning High-Dimensional Statistics


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	60	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	39	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
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
The mean-field approximation: Information inequalities, algorithms, and complexity V Jain, F Koehler, E Mossel Conference On Learning Theory, 1326-1347, 2018	31	2018
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
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
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	20	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

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