Optimal Batch Schedules for Parallel Machines. - Frederic Koehler; Samir Khuller (WADS, 2013)

Provable Algorithms for Inference in Topic Models. - Sanjeev Arora; Rong Ge 001; Frederic Koehler; Tengyu Ma 001; Ankur Moitra (ICML, 2016)

Information Theoretic Properties of Markov Random Fields, and their Algorithmic Applications. - Linus Hamilton; Frederic Koehler; Ankur Moitra (NIPS, 2017)

Busy Time Scheduling on a Bounded Number of Machines (Extended Abstract). - Frederic Koehler; Samir Khuller (WADS, 2017)

Approximating Partition Functions in Constant Time. - Vishesh Jain; Frederic Koehler; Elchanan Mossel (CoRR, 2017)

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 (COLT, 2018)

Learning Restricted Boltzmann Machines via Influence Maximization. - Guy Bresler; Frederic Koehler; Ankur Moitra; Elchanan Mossel (CoRR, 2018)

Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis. - Frederic Koehler; Andrej Risteski (CoRR, 2018)

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

The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure. - Frederic Koehler; Andrej Risteski (ICLR (Poster), 2019)

Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay. - Frederic Koehler (NeurIPS, 2019)

Learning restricted Boltzmann machines via influence maximization. - Guy Bresler; Frederic Koehler; Ankur Moitra (STOC, 2019)

Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. - Vishesh Jain; Frederic Koehler; Andrej Risteski (STOC, 2019)

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 R. Klivans; Frederic Koehler (NeurIPS, 2020)

Learning Some Popular Gaussian Graphical Models without Condition Number Bounds. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Ankur Moitra (NeurIPS, 2020)

A Phase Transition in Arrow's Theorem. - Frederic Koehler; Elchanan Mossel (CoRR, 2020)

Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. - Sitan Chen; Frederic Koehler; Ankur Moitra; Morris Yau (CoRR, 2020)

How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories. - Younhun Kim; Frederic Koehler; Ankur Moitra; Elchanan Mossel; Govind Ramnarayan (J. Comput. Biol., 2020)

Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. - Enric Boix-Adserà; Guy Bresler; Frederic Koehler (FOCS, 2021)

Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. - Sitan Chen; Frederic Koehler; Ankur Moitra; Morris Yau (FOCS, 2021)

On the Power of Preconditioning in Sparse Linear Regression. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (FOCS, 2021)

Multidimensional Scaling: Approximation and Complexity. - Erik D. Demaine; Adam Hesterberg; Frederic Koehler; Jayson Lynch; John C. Urschel (ICML, 2021)

Representational aspects of depth and conditioning in normalizing flows. - Frederic Koehler; Viraj Mehta; Andrej Risteski (ICML, 2021)

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

Entropic Independence in High-Dimensional Expanders: Modified Log-Sobolev Inequalities for Fractionally Log-Concave Polynomials and the Ising Model. - Nima Anari; Vishesh Jain; Frederic Koehler; Huy Tuan Pham; Thuy-Duong Vuong (CoRR, 2021)

Entropic Independence II: Optimal Sampling and Concentration via Restricted Modified Log-Sobolev Inequalities. - Nima Anari; Vishesh Jain; Frederic Koehler; Huy Tuan Pham; Thuy-Duong Vuong (CoRR, 2021)

Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression. - Lijia Zhou; Frederic Koehler; Danica J. Sutherland; Nathan Srebro (CoRR, 2021)

Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. - Frederic Koehler; Holden Lee; Andrej Risteski (COLT, 2022)

Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. - Frederic Koehler; Viraj Mehta; Chenghui Zhou; Andrej Risteski (ICLR, 2022)

Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (NeurIPS, 2022)

Reconstruction on Trees and Low-Degree Polynomials. - Frederic Koehler; Elchanan Mossel (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)

Entropic independence: optimal mixing of down-up random walks. - Nima Anari; Vishesh Jain; Frederic Koehler; Huy Tuan Pham; Thuy-Duong Vuong (STOC, 2022)

Kalman filtering with adversarial corruptions. - Sitan Chen; Frederic Koehler; Ankur Moitra; Morris Yau (STOC, 2022)

Distributional Hardness Against Preconditioned Lasso via Erasure-Robust Designs. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (CoRR, 2022)

Statistical Efficiency of Score Matching: The View from Isoperimetry. - Frederic Koehler; Alexander Heckett; Andrej Risteski (ICLR, 2023)

Feature Adaptation for Sparse Linear Regression. - Jonathan A. Kelner; Frederic Koehler; Raghu Meka; Dhruv Rohatgi (NeurIPS, 2023)

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

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

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

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

Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses. - Nima Anari; Vishesh Jain; Frederic Koehler; Huy Tuan Pham; Thuy-Duong Vuong (SODA, 2024)

Trickle-Down in Localization Schemes and Applications. - Nima Anari; Frederic Koehler; Thuy-Duong Vuong (STOC, 2024)

Influences in Mixing Measures. - Frederic Koehler; Noam Lifshitz; Dor Minzer; Elchanan Mossel (STOC, 2024)

Efficiently learning and sampling multimodal distributions with data-based initialization. - Frederic Koehler; Holden Lee; Thuy-Duong Vuong (COLT, 2025)