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ORCIDFrederic Koehler
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2020 – today
- 2024
[c37]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. COLT 2024: 2840-2886- [c36]Frederic Koehler, Thuy-Duong Vuong:
Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization. ICLR 2024 - [c35]Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander:
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting. ICML 2024 - [c34]Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong:
Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses. SODA 2024: 5029-5056 - [c33]Frederic Koehler
, Noam Lifshitz, Dor Minzer, Elchanan Mossel:
Influences in Mixing Measures. STOC 2024: 527-536 - [c32]Nima Anari, Frederic Koehler, Thuy-Duong Vuong:
Trickle-Down in Localization Schemes and Applications. STOC 2024: 1094-1105 - [i38]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. CoRR abs/2402.15409 (2024) - [i37]Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander:
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting. CoRR abs/2402.18697 (2024) - [i36]Nima Anari, Frederic Koehler, Thuy-Duong Vuong:
Trickle-Down in Localization Schemes and Applications. CoRR abs/2407.16104 (2024) - 2023
- [c31]Frederic Koehler, Alexander Heckett, Andrej Risteski:
Statistical Efficiency of Score Matching: The View from Isoperimetry. ICLR 2023 - [c30]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Feature Adaptation for Sparse Linear Regression. NeurIPS 2023 - [c29]Lijia Zhou, Zhen Dai, Frederic Koehler, Nati Srebro:
Uniform Convergence with Square-Root Lipschitz Loss. NeurIPS 2023 - [i35]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Feature Adaptation for Sparse Linear Regression. CoRR abs/2305.16892 (2023) - [i34]Lijia Zhou, Zhen Dai, Frederic Koehler, Nathan Srebro:
Uniform Convergence with Square-Root Lipschitz Loss. CoRR abs/2306.13188 (2023) - [i33]Frederic Koehler, Noam Lifshitz, Dor Minzer, Elchanan Mossel:
Influences in Mixing Measures. CoRR abs/2307.07625 (2023) - [i32]Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong:
Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses. CoRR abs/2307.10466 (2023) - [i31]Frederic Koehler, Thuy-Duong Vuong:
Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization. CoRR abs/2310.01762 (2023) - 2022
- [c28]Frederic Koehler, Holden Lee, Andrej Risteski:
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. COLT 2022: 4945-4988 - [c27]Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski:
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. ICLR 2022 - [c26]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. NeurIPS 2022 - [c25]Frederic Koehler, Elchanan Mossel:
Reconstruction on Trees and Low-Degree Polynomials. NeurIPS 2022 - [c24]Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro:
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. NeurIPS 2022 - [c23]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Kalman filtering with adversarial corruptions. STOC 2022: 832-845 - [c22]Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong:
Entropic independence: optimal mixing of down-up random walks. STOC 2022: 1418-1430 - [i30]Frederic Koehler, Holden Lee, Andrej Risteski:
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. CoRR abs/2202.08907 (2022) - [i29]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs. CoRR abs/2203.02824 (2022) - [i28]Frederic Koehler, Alexander Heckett, Andrej Risteski:
Statistical Efficiency of Score Matching: The View from Isoperimetry. CoRR abs/2210.00726 (2022) - [i27]Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro:
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR abs/2210.12082 (2022) - 2021
- [c21]Enric Boix-Adserà, Guy Bresler, Frederic Koehler:
Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. FOCS 2021: 417-426 - [c20]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
On the Power of Preconditioning in Sparse Linear Regression. FOCS 2021: 550-561 - [c19]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS 2021: 684-695 - [c18]Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John C. Urschel:
Multidimensional Scaling: Approximation and Complexity. ICML 2021: 2568-2578 - [c17]Frederic Koehler, Viraj Mehta, Andrej Risteski:
Representational aspects of depth and conditioning in normalizing flows. ICML 2021: 5628-5636 - [c16]Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro:
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. NeurIPS 2021: 20657-20668 - [i26]Enric Boix-Adserà, Guy Bresler, Frederic Koehler:
Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. CoRR abs/2106.03969 (2021) - [i25]Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong:
Entropic Independence in High-Dimensional Expanders: Modified Log-Sobolev Inequalities for Fractionally Log-Concave Polynomials and the Ising Model. CoRR abs/2106.04105 (2021) - [i24]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi:
On the Power of Preconditioning in Sparse Linear Regression. CoRR abs/2106.09207 (2021) - [i23]Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro:
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting. CoRR abs/2106.09276 (2021) - [i22]Frederic Koehler, Elchanan Mossel:
Reconstruction on Trees and Low-Degree Polynomials. CoRR abs/2109.06915 (2021) - [i21]Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John C. Urschel:
Multidimensional Scaling: Approximation and Complexity. CoRR abs/2109.11505 (2021) - [i20]Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong:
Entropic Independence II: Optimal Sampling and Concentration via Restricted Modified Log-Sobolev Inequalities. CoRR abs/2111.03247 (2021) - [i19]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Kalman Filtering with Adversarial Corruptions. CoRR abs/2111.06395 (2021) - [i18]Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro:
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. CoRR abs/2112.04470 (2021) - [i17]Frederic Koehler, Viraj Mehta, Andrej Risteski, Chenghui Zhou:
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. CoRR abs/2112.06868 (2021) - 2020
- [j1]Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel, Govind Ramnarayan:
How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories. J. Comput. Biol. 27(4): 613-625 (2020) - [c15]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. NeurIPS 2020 - [c14]Surbhi Goel, Adam R. Klivans, Frederic Koehler:
From Boltzmann Machines to Neural Networks and Back Again. NeurIPS 2020 - [c13]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra:
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. NeurIPS 2020 - [i16]Frederic Koehler, Elchanan Mossel:
A Phase Transition in Arrow's Theorem. CoRR abs/2004.12580 (2020) - [i15]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. CoRR abs/2006.04787 (2020) - [i14]Surbhi Goel, Adam R. Klivans, Frederic Koehler:
From Boltzmann Machines to Neural Networks and Back Again. CoRR abs/2007.12815 (2020) - [i13]Frederic Koehler, Viraj Mehta, Andrej Risteski:
Representational aspects of depth and conditioning in normalizing flows. CoRR abs/2010.01155 (2020) - [i12]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. CoRR abs/2010.04157 (2020)
2010 – 2019
- 2019
- [c12]Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel:
Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation. COLT 2019: 1756-1771 - [c11]Frederic Koehler, Andrej Risteski:
The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure. ICLR (Poster) 2019 - [c10]Frederic Koehler:
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. NeurIPS 2019: 8329-8339 - [c9]Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel, Govind Ramnarayan:
How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories. RECOMB 2019: 136-157 - [c8]Guy Bresler, Frederic Koehler, Ankur Moitra:
Learning restricted Boltzmann machines via influence maximization. STOC 2019: 828-839 - [c7]Vishesh Jain, Frederic Koehler, Andrej Risteski:
Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. STOC 2019: 1226-1236 - [i11]Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra:
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. CoRR abs/1905.01282 (2019) - [i10]Frederic Koehler:
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. CoRR abs/1905.09992 (2019) - [i9]Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel:
Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation. CoRR abs/1905.10031 (2019) - 2018
- [c6]Vishesh Jain, Frederic Koehler, Elchanan Mossel:
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity. COLT 2018: 1326-1347 - [c5]Vishesh Jain, Frederic Koehler, Elchanan Mossel:
The Vertex Sample Complexity of Free Energy is Polynomial. COLT 2018: 1395-1419 - [i8]Vishesh Jain, Frederic Koehler, Elchanan Mossel:
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity. CoRR abs/1802.06126 (2018) - [i7]Vishesh Jain, Frederic Koehler, Elchanan Mossel:
The Vertex Sample Complexity of Free Energy is Polynomial. CoRR abs/1802.06129 (2018) - [i6]Guy Bresler, Frederic Koehler, Ankur Moitra, Elchanan Mossel:
Learning Restricted Boltzmann Machines via Influence Maximization. CoRR abs/1805.10262 (2018) - [i5]Frederic Koehler, Andrej Risteski:
Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis. CoRR abs/1805.11405 (2018) - [i4]Vishesh Jain, Frederic Koehler, Andrej Risteski:
Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. CoRR abs/1808.07226 (2018) - 2017
- [c4]Linus Hamilton, Frederic Koehler, Ankur Moitra:
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications. NIPS 2017: 2463-2472 - [c3]Frederic Koehler, Samir Khuller:
Busy Time Scheduling on a Bounded Number of Machines (Extended Abstract). WADS 2017: 521-532 - [i3]Linus Hamilton, Frederic Koehler, Ankur Moitra:
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications. CoRR abs/1705.11107 (2017) - [i2]Vishesh Jain, Frederic Koehler, Elchanan Mossel:
Approximating Partition Functions in Constant Time. CoRR abs/1711.01655 (2017) - 2016
- [c2]Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra:
Provable Algorithms for Inference in Topic Models. ICML 2016: 2859-2867 - [i1]Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra:
Provable Algorithms for Inference in Topic Models. CoRR abs/1605.08491 (2016) - 2013
- [c1]Frederic Koehler, Samir Khuller:
Optimal Batch Schedules for Parallel Machines. WADS 2013: 475-486
Coauthor Index
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last updated on 2024-09-04 01:20 CEST by the dblp team
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