Koehler, Frederic

27 publications

COLT 2025 Efficiently Learning and Sampling Multimodal Distributions with Data-Based Initialization Frederic Koehler, Holden Lee, Thuy-Duong Vuong

ICML 2024 Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander

COLT 2024 Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi

ICLR 2024 Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization Frederic Koehler, Thuy-Duong Vuong

NeurIPS 2023 Feature Adaptation for Sparse Linear Regression Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi

ICLR 2023 Statistical Efficiency of Score Matching: The View from Isoperimetry Frederic Koehler, Alexander Heckett, Andrej Risteski

NeurIPS 2023 Uniform Convergence with Square-Root Lipschitz Loss Lijia Zhou, Zhen Dai, Frederic Koehler, Nati Srebro

NeurIPS 2022 A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models Lijia Zhou, Frederic Koehler, Pragya Sur, Danica J. Sutherland, Nati Srebro

NeurIPS 2022 Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs Jonathan Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi

NeurIPS 2022 Reconstruction on Trees and Low-Degree Polynomials Frederic Koehler, Elchanan Mossel

COLT 2022 Sampling Approximately Low-Rank Ising Models: MCMC Meets Variational Methods Frederic Koehler, Holden Lee, Andrej Risteski

NeurIPSW 2022 Statistical Efficiency of Score Matching: The View from Isoperimetry Frederic Koehler, Alexander Heckett, Andrej Risteski

ICLR 2022 Variational Autoencoders in the Presence of Low-Dimensional Data: Landscape and Implicit Bias Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski

ICML 2021 Multidimensional Scaling: Approximation and Complexity Erik Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel

ICML 2021 Representational Aspects of Depth and Conditioning in Normalizing Flows Frederic Koehler, Viraj Mehta, Andrej Risteski

ICMLW 2021 Representational Aspects of Depth and Conditioning in Normalizing Flows Frederic Koehler, Viraj Mehta, Andrej Risteski

NeurIPS 2021 Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting Frederic Koehler, Lijia Zhou, Danica J. Sutherland, Nathan Srebro

NeurIPS 2020 Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau

NeurIPS 2020 From Boltzmann Machines to Neural Networks and Back Again Surbhi Goel, Adam Klivans, Frederic Koehler

NeurIPS 2020 Learning Some Popular Gaussian Graphical Models Without Condition Number Bounds Jonathan Kelner, Frederic Koehler, Raghu Meka, Ankur Moitra

COLT 2019 Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel

NeurIPS 2019 Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay Frederic Koehler

ICLR 2019 The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure Frederic Koehler, Andrej Risteski

COLT 2018 The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity Vishesh Jain, Frederic Koehler, Elchanan Mossel

COLT 2018 The Vertex Sample Complexity of Free Energy Is Polynomial Vishesh Jain, Frederic Koehler, Elchanan Mossel

NeurIPS 2017 Information Theoretic Properties of Markov Random Fields, and Their Algorithmic Applications Linus Hamilton, Frederic Koehler, Ankur Moitra

ICML 2016 Provable Algorithms for Inference in Topic Models Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra