Frederic Koehler

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 77

Top Topics

Generalized Linear Models

Boltzmann Machines

Uniform Convergence

Top Venues

Publications

Frederic Koehler, Noam Lifshitz, Dor Minzer, Elchanan Mossel
Influences in Mixing Measures. STOC (2024)
Frederic Koehler, Thuy-Duong Vuong
Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization. ICLR (2024)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. COLT (2024)
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)
Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander
Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting. CoRR (2024)
Nima Anari, Frederic Koehler, Thuy-Duong Vuong
Trickle-Down in Localization Schemes and Applications. STOC (2024)
Nima Anari, Frederic Koehler, Thuy-Duong Vuong
Trickle-Down in Localization Schemes and Applications. CoRR (2024)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. CoRR (2024)
Lijia Zhou, Zhen Dai, Frederic Koehler, Nati Srebro
Uniform Convergence with Square-Root Lipschitz Loss. NeurIPS (2023)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
Feature Adaptation for Sparse Linear Regression. NeurIPS (2023)
Frederic Koehler, Alexander Heckett, Andrej Risteski
Statistical Efficiency of Score Matching: The View from Isoperimetry. ICLR (2023)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
Feature Adaptation for Sparse Linear Regression. CoRR (2023)
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 (2023)
Frederic Koehler, Thuy-Duong Vuong
Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization. CoRR (2023)
Lijia Zhou, Zhen Dai, Frederic Koehler, Nathan Srebro
Uniform Convergence with Square-Root Lipschitz Loss. CoRR (2023)
Frederic Koehler, Noam Lifshitz, Dor Minzer, Elchanan Mossel
Influences in Mixing Measures. CoRR (2023)
Frederic Koehler, Elchanan Mossel
Reconstruction on Trees and Low-Degree Polynomials. NeurIPS (2022)
Frederic Koehler, Holden Lee, Andrej Risteski
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. CoRR (2022)
Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. ICLR (2022)
Enric Boix-Adserà, Guy Bresler, Frederic Koehler
Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. FOCS (2022)
Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong
Entropic independence: optimal mixing of down-up random walks. STOC (2022)
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)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. NeurIPS (2022)
Frederic Koehler, Holden Lee, Andrej Risteski
Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. COLT (2022)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS (2022)
Frederic Koehler, Alexander Heckett, Andrej Risteski
Statistical Efficiency of Score Matching: The View from Isoperimetry. CoRR (2022)
Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro
A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR (2022)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Kalman filtering with adversarial corruptions. STOC (2022)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs. CoRR (2022)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
On the Power of Preconditioning in Sparse Linear Regression. FOCS (2022)
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 (2021)
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 (2021)
Enric Boix-Adserà, Guy Bresler, Frederic Koehler
Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. CoRR (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS (2021)
Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting. CoRR (2021)
Frederic Koehler, Elchanan Mossel
Reconstruction on Trees and Low-Degree Polynomials. CoRR (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Kalman Filtering with Adversarial Corruptions. CoRR (2021)
Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. NeurIPS (2021)
Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John C. Urschel
Multidimensional Scaling: Approximation and Complexity. CoRR (2021)
Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John C. Urschel
Multidimensional Scaling: Approximation and Complexity. ICML (2021)
Frederic Koehler, Viraj Mehta, Andrej Risteski
Representational aspects of depth and conditioning in normalizing flows. ICML (2021)
Enric Boix-Adserà, Guy Bresler, Frederic Koehler
Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. FOCS (2021)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
On the Power of Preconditioning in Sparse Linear Regression. CoRR (2021)
Frederic Koehler, Viraj Mehta, Andrej Risteski, Chenghui Zhou
Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. CoRR (2021)
Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro
Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. CoRR (2021)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
On the Power of Preconditioning in Sparse Linear Regression. FOCS (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. CoRR (2020)
Surbhi Goel, Adam R. Klivans, Frederic Koehler
From Boltzmann Machines to Neural Networks and Back Again. NeurIPS (2020)
Surbhi Goel, Adam R. Klivans, Frederic Koehler
From Boltzmann Machines to Neural Networks and Back Again. CoRR (2020)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. NeurIPS (2020)
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) (2020)
Frederic Koehler, Viraj Mehta, Andrej Risteski
Representational aspects of depth and conditioning in normalizing flows. CoRR (2020)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. CoRR (2020)
Frederic Koehler, Elchanan Mossel
A Phase Transition in Arrow's Theorem. CoRR (2020)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. NeurIPS (2020)
Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel
Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation. COLT (2019)
Vishesh Jain, Frederic Koehler, Andrej Risteski
Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. STOC (2019)
Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel
Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation. CoRR (2019)
Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra
Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. CoRR (2019)
Frederic Koehler, Andrej Risteski
The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure. ICLR (Poster) (2019)
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)
Frederic Koehler
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. NeurIPS (2019)
Guy Bresler, Frederic Koehler, Ankur Moitra
Learning restricted Boltzmann machines via influence maximization. STOC (2019)
Frederic Koehler
Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. CoRR (2019)
Vishesh Jain, Frederic Koehler, Elchanan Mossel
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity. CoRR (2018)
Vishesh Jain, Frederic Koehler, Elchanan Mossel
The Vertex Sample Complexity of Free Energy is Polynomial. CoRR (2018)
Vishesh Jain, Frederic Koehler, Andrej Risteski
Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. CoRR (2018)
Guy Bresler, Frederic Koehler, Ankur Moitra, Elchanan Mossel
Learning Restricted Boltzmann Machines via Influence Maximization. CoRR (2018)
Frederic Koehler, Andrej Risteski
Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis. CoRR (2018)
Vishesh Jain, Frederic Koehler, Elchanan Mossel
The Vertex Sample Complexity of Free Energy is Polynomial. COLT (2018)
Vishesh Jain, Frederic Koehler, Elchanan Mossel
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity. COLT (2018)
Vishesh Jain, Frederic Koehler, Elchanan Mossel
Approximating Partition Functions in Constant Time. CoRR (2017)
Linus Hamilton, Frederic Koehler, Ankur Moitra
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications. CoRR (2017)
Frederic Koehler, Samir Khuller
Busy Time Scheduling on a Bounded Number of Machines (Extended Abstract). WADS (2017)
Linus Hamilton, Frederic Koehler, Ankur Moitra
Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications. NIPS (2017)
Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
Provable Algorithms for Inference in Topic Models. CoRR (2016)
Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
Provable Algorithms for Inference in Topic Models. ICML (2016)
Frederic Koehler, Samir Khuller
Optimal Batch Schedules for Parallel Machines. WADS (2013)