| 2026 | COLT | Overlap Analysis of the Shortest Path Problem: Local Search, Landscapes, and Franz-Parisi Potential. | Frederic Koehler, Joonhyung Shin |
| 2026 | STOC | Parallel Sampling via Autospeculation. | Nima Anari, Carlo Baronio, CJ Chen, Alireza Haqi, Frederic Koehler, Anqi Li, Thuy-Duong Vuong |
| 2026 | STOC | Constructive Approximation under Carleman's Condition, with Applications to Smoothed Analysis. | Frederic Koehler, Beining Wu |
| 2025 | COLT | Efficiently learning and sampling multimodal distributions with data-based initialization. | Frederic Koehler, Holden Lee, Thuy-Duong Vuong |
| 2024 | COLT | Lasso with Latents: Efficient Estimation, Covariate Rescaling, and Computational-Statistical Gaps. | Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi |
| 2024 | ICLR | Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization. | Frederic Koehler, Thuy-Duong Vuong |
| 2024 | ICML | Inferring Dynamic Networks from Marginals with Iterative Proportional Fitting. | Serina Chang, Frederic Koehler, Zhaonan Qu, Jure Leskovec, Johan Ugander |
| 2024 | SODA | Universality of Spectral Independence with Applications to Fast Mixing in Spin Glasses. | Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong |
| 2024 | STOC | Trickle-Down in Localization Schemes and Applications. | Nima Anari, Frederic Koehler, Thuy-Duong Vuong |
| 2024 | STOC | Influences in Mixing Measures. | Frederic Koehler, Noam Lifshitz, Dor Minzer, Elchanan Mossel |
| 2023 | ICLR | Statistical Efficiency of Score Matching: The View from Isoperimetry. | Frederic Koehler, Alexander Heckett, Andrej Risteski |
| 2022 | COLT | Sampling Approximately Low-Rank Ising Models: MCMC meets Variational Methods. | Frederic Koehler, Holden Lee, Andrej Risteski |
| 2022 | ICLR | Variational autoencoders in the presence of low-dimensional data: landscape and implicit bias. | Frederic Koehler, Viraj Mehta, Chenghui Zhou, Andrej Risteski |
| 2022 | STOC | Entropic independence: optimal mixing of down-up random walks. | Nima Anari, Vishesh Jain, Frederic Koehler, Huy Tuan Pham, Thuy-Duong Vuong |
| 2022 | STOC | Kalman filtering with adversarial corruptions. | Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau |
| 2021 | FOCS | Chow-Liu++: Optimal Prediction-Centric Learning of Tree Ising Models. | Enric Boix-Adser, Guy Bresler, Frederic Koehler |
| 2021 | FOCS | Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. | Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau |
| 2021 | FOCS | On the Power of Preconditioning in Sparse Linear Regression. | Jonathan A. Kelner, Frederic Koehler, Raghu Meka, Dhruv Rohatgi |
| 2021 | ICML | Multidimensional Scaling: Approximation and Complexity. | Erik D. Demaine, Adam Hesterberg, Frederic Koehler, Jayson Lynch, John Urschel |
| 2021 | ICML | Representational aspects of depth and conditioning in normalizing flows. | Frederic Koehler, Viraj Mehta, Andrej Risteski |
| 2019 | COLT | Accuracy-Memory Tradeoffs and Phase Transitions in Belief Propagation. | Vishesh Jain, Frederic Koehler, Jingbo Liu, Elchanan Mossel |
| 2019 | ICLR | The Comparative Power of ReLU Networks and Polynomial Kernels in the Presence of Sparse Latent Structure. | Frederic Koehler, Andrej Risteski |
| 2019 | RECOMB | How Many Subpopulations Is Too Many? Exponential Lower Bounds for Inferring Population Histories. | Younhun Kim, Frederic Koehler, Ankur Moitra, Elchanan Mossel, Govind Ramnarayan |
| 2019 | STOC | Learning restricted Boltzmann machines via influence maximization. | Guy Bresler, Frederic Koehler, Ankur Moitra |
| 2019 | STOC | Mean-field approximation, convex hierarchies, and the optimality of correlation rounding: a unified perspective. | Vishesh Jain, Frederic Koehler, Andrej Risteski |
| 2018 | COLT | The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity. | Vishesh Jain, Frederic Koehler, Elchanan Mossel |
| 2018 | COLT | The Vertex Sample Complexity of Free Energy is Polynomial. | Vishesh Jain, Frederic Koehler, Elchanan Mossel |
| 2017 | WADS | Busy Time Scheduling on a Bounded Number of Machines (Extended Abstract). | Frederic Koehler, Samir Khuller |
| 2016 | ICML | Provable Algorithms for Inference in Topic Models. | Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra |
| 2013 | WADS | Optimal Batch Schedules for Parallel Machines. | Frederic Koehler, Samir Khuller |