Frederic Koehler

81 papers A* 33B 3Journal 43Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Constructive Approximation under Carleman's Condition, with Applications to Smoothed Analysis. CoRR Frederic Koehler, Beining Wu
2025	A*	`conf`	Efficiently learning and sampling multimodal distributions with data-based initialization. COLT Frederic Koehler, Holden Lee, Thuy-Duong Vuong
2025	J	`jnl`	Overlap Analysis of the Shortest Path Problem: Local Search, Landscapes, and Franz-Parisi Potential. CoRR Frederic Koehler, Joonhyung Shin
2025	J	`jnl`	Parallel Sampling via Autospeculation. CoRR Nima Anari, Carlo Baronio, CJ Chen, Alireza Haqi, Frederic Koehler, Anqi Li, Thuy-Duong Vuong
2024	J	`jnl`	Efficiently learning and sampling multimodal distributions with data-based initialization. CoRR Frederic Koehler, Holden Lee, Thuy-Duong Vuong
2024	A*	`conf`	Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting. ICML Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander
2024	J	`jnl`	Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting. CoRR Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander
2024	A*	`conf`	Influences in Mixing Measures. STOC Frederic Koehler, Noam Lifshitz, Dor Minzer, Elchanan Mossel
2024	A*	`conf`	Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. COLT Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
2024	J	`jnl`	Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. CoRR Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
2024	A*	`conf`	Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization. ICLR Frederic Koehler, Thuy-Duong Vuong
2024	A*	`conf`	Trickle-Down in Localization Schemes and Applications. STOC Nima Anari, Frederic Koehler, Thuy-Duong Vuong
2024	J	`jnl`	Trickle-Down in Localization Schemes and Applications. CoRR Nima Anari, Frederic Koehler, Thuy-Duong Vuong
2024	A*	`conf`	Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses. SODA Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong
2023	A*	`conf`	Feature Adaptation for Sparse Linear Regression. NeurIPS Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
2023	J	`jnl`	Feature Adaptation for Sparse Linear Regression. CoRR Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
2023	J	`jnl`	Influences in Mixing Measures. CoRR Frederic Koehler, Noam Lifshitz, Dor Minzer, Elchanan Mossel
2023	J	`jnl`	Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization. CoRR Frederic Koehler, Thuy-Duong Vuong
2023	A*	`conf`	Statistical Efficiency of Score Matching: The View from Isoperimetry. ICLR Frederic Koehler, Alexander Heckett, Andrej Risteski
2023	A*	`conf`	Uniform Convergence with Square-Root Lipschitz Loss. NeurIPS Lijia Zhou, Zhen Dai, Frederic Koehler, Nati Srebro
2023	J	`jnl`	Uniform Convergence with Square-Root Lipschitz Loss. CoRR Lijia Zhou, Zhen Dai, Frederic Koehler, Nathan Srebro
2023	J	`jnl`	Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses. CoRR Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong
2022	A*	`conf`	A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. NeurIPS Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro
2022	J	`jnl`	A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models. CoRR Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nathan Srebro
2022	J	`jnl`	Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs. CoRR Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
2022	A*	`conf`	Entropic independence: optimal mixing of down-up random walks. STOC Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong
2022	A*	`conf`	Kalman filtering with adversarial corruptions. STOC Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2022	A*	`conf`	Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. NeurIPS Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
2022	A*	`conf`	Reconstruction on Trees and Low-Degree Polynomials. NeurIPS Frederic Koehler, Elchanan Mossel
2022	A*	`conf`	Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. COLT Frederic Koehler, Holden Lee, Andrej Risteski
2022	J	`jnl`	Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. CoRR Frederic Koehler, Holden Lee, Andrej Risteski
2022	J	`jnl`	Statistical Efficiency of Score Matching: The View from Isoperimetry. CoRR Frederic Koehler, Alexander Heckett, Andrej Risteski
2022	A*	`conf`	Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. ICLR Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski
2021	A*	`conf`	Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. FOCS Enric Boix-Adserà, Guy Bresler, Frederic Koehler
2021	J	`jnl`	Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. CoRR Enric Boix-Adserà, Guy Bresler, Frederic Koehler
2021	J	`jnl`	Entropic Independence II: Optimal Sampling and Concentration via Restricted Modified Log-Sobolev Inequalities. CoRR Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong
2021	J	`jnl`	Entropic Independence in High-Dimensional Expanders: Modified Log-Sobolev Inequalities for Fractionally Log-Concave Polynomials and the Ising Model. CoRR Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong
2021	J	`jnl`	Kalman Filtering with Adversarial Corruptions. CoRR Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2021	A*	`conf`	Multidimensional Scaling: Approximation and Complexity. ICML Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel
2021	J	`jnl`	Multidimensional Scaling: Approximation and Complexity. CoRR Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel
2021	A*	`conf`	On the Power of Preconditioning in Sparse Linear Regression. FOCS Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
2021	J	`jnl`	On the Power of Preconditioning in Sparse Linear Regression. CoRR Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi
2021	A*	`conf`	Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2021	J	`jnl`	Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. CoRR Lijia Zhou, Frederic Koehler, Danica J. Sutherland, Nathan Srebro
2021	J	`jnl`	Reconstruction on Trees and Low-Degree Polynomials. CoRR Frederic Koehler, Elchanan Mossel
2021	A*	`conf`	Representational aspects of depth and conditioning in normalizing flows. ICML Frederic Koehler, Viraj Mehta, Andrej Risteski
2021	A*	`conf`	Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting. NeurIPS Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
2021	J	`jnl`	Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting. CoRR Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro
2021	J	`jnl`	Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. CoRR Frederic Koehler, Viraj Mehta, Andrej Risteski, Chenghui Zhou
2020	J	`jnl`	A Phase Transition in Arrow's Theorem. CoRR Frederic Koehler, Elchanan Mossel
2020	J	`jnl`	Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. CoRR Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2020	A*	`conf`	Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. NeurIPS Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2020	A*	`conf`	From Boltzmann Machines to Neural Networks and Back Again. NeurIPS Surbhi Goel, Adam R. Klivans, Frederic Koehler
2020	J	`jnl`	From Boltzmann Machines to Neural Networks and Back Again. CoRR Surbhi Goel, Adam R. Klivans, Frederic Koehler
2020	J	`jnl`	How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories. J. Comput. Biol. Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel, Govind Ramnarayan
2020	A*	`conf`	Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. NeurIPS Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra
2020	J	`jnl`	Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. CoRR Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2020	J	`jnl`	Representational aspects of depth and conditioning in normalizing flows. CoRR Frederic Koehler, Viraj Mehta, Andrej Risteski
2019	A*	`conf`	Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation. COLT Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel
2019	J	`jnl`	Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation. CoRR Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel
2019	A*	`conf`	Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. NeurIPS Frederic Koehler
2019	J	`jnl`	Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. CoRR Frederic Koehler
2019	B	`conf`	How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories. RECOMB Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel, Govind Ramnarayan
2019	J	`jnl`	Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. CoRR Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra
2019	A*	`conf`	Learning restricted Boltzmann machines via influence maximization. STOC Guy Bresler, Frederic Koehler, Ankur Moitra
2019	A*	`conf`	Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. STOC Vishesh Jain, Frederic Koehler, Andrej Risteski
2019	—	`conf`	The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure. ICLR (Poster) Frederic Koehler, Andrej Risteski
2018	J	`jnl`	Learning Restricted Boltzmann Machines via Influence Maximization. CoRR Guy Bresler, Frederic Koehler, Ankur Moitra, Elchanan Mossel
2018	J	`jnl`	Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. CoRR Vishesh Jain, Frederic Koehler, Andrej Risteski
2018	J	`jnl`	Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis. CoRR Frederic Koehler, Andrej Risteski
2018	A*	`conf`	The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity. COLT Vishesh Jain, Frederic Koehler, Elchanan Mossel
2018	J	`jnl`	The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity. CoRR Vishesh Jain, Frederic Koehler, Elchanan Mossel
2018	A*	`conf`	The Vertex Sample Complexity of Free Energy is Polynomial. COLT Vishesh Jain, Frederic Koehler, Elchanan Mossel
2018	J	`jnl`	The Vertex Sample Complexity of Free Energy is Polynomial. CoRR Vishesh Jain, Frederic Koehler, Elchanan Mossel
2017	J	`jnl`	Approximating Partition Functions in Constant Time. CoRR Vishesh Jain, Frederic Koehler, Elchanan Mossel
2017	B	`conf`	Busy Time Scheduling on a Bounded Number of Machines (Extended Abstract). WADS Frederic Koehler, Samir Khuller
2017	—	`conf`	Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications. NIPS Linus Hamilton, Frederic Koehler, Ankur Moitra
2017	J	`jnl`	Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications. CoRR Linus Hamilton, Frederic Koehler, Ankur Moitra
2016	A*	`conf`	Provable Algorithms for Inference in Topic Models. ICML Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
2016	J	`jnl`	Provable Algorithms for Inference in Topic Models. CoRR Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra
2013	B	`conf`	Optimal Batch Schedules for Parallel Machines. WADS Frederic Koehler, Samir Khuller