| 2026 | COLT | Learning Ising Models from Evolutions (Extended Abstract). | Jason Gaitonde, Ankur Moitra, Elchanan Mossel |
| 2026 | COLT | Steering diffusion models with quadratic rewards: a fine-grained analysis. | Ankur Moitra, Andrej Risteski, Dhruv Rohatgi |
| 2026 | STOC | A Dobrushin Condition for Quantum Markov Chains: Rapid Mixing and Conditional Mutual Information at High Temperature. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2026 | STOC | Improved Pseudorandom Codes from Permuted Puzzles. | Miranda Christ, Noah Golowich, Sam Gunn, Ankur Moitra, Daniel Wichs |
| 2025 | COLT | Conference on Learning Theory 2025: Preface. | Nika Haghtalab, Ankur Moitra |
| 2025 | FOCS | Overcomplete Tensor Decomposition via Koszul-Young Flattenings. | Pravesh K. Kothari, Ankur Moitra, Alexander S. Wein |
| 2025 | ICML | Towards characterizing the value of edge embeddings in Graph Neural Networks. | Dhruv Rohatgi, Tanya Marwah, Zachary Chase Lipton, Jianfeng Lu, Ankur Moitra, Andrej Risteski |
| 2025 | STOC | Bypassing the Noisy Parity Barrier: Learning Higher-Order Markov Random Fields from Dynamics. | Jason Gaitonde, Ankur Moitra, Elchanan Mossel |
| 2025 | STOC | Model Stealing for Any Low-Rank Language Model. | Allen Liu, Ankur Moitra |
| 2024 | COLT | The power of an adversary in Glauber dynamics. | Byron Chin, Ankur Moitra, Elchanan Mossel, Colin Sandon |
| 2024 | COLT | Linear Bellman Completeness Suffices for Efficient Online Reinforcement Learning with Few Actions. | Noah Golowich, Ankur Moitra |
| 2024 | FOCS | High-Temperature Gibbs States are Unentangled and Efficiently Preparable. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2024 | FOCS | Structure Learning of Hamiltonians from Real-Time Evolution. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2024 | FOCS | Exploration is Harder than Prediction: Cryptographically Separating Reinforcement Learning from Supervised Learning. | Noah Golowich, Ankur Moitra, Dhruv Rohatgi |
| 2024 | STOC | Learning Quantum Hamiltonians at Any Temperature in Polynomial Time. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2024 | STOC | Exploring and Learning in Sparse Linear MDPs without Computationally Intractable Oracles. | Noah Golowich, Ankur Moitra, Dhruv Rohatgi |
| 2023 | FOCS | Strong Spatial Mixing for Colorings on Trees and its Algorithmic Applications. | Zongchen Chen, Kuikui Liu, Nitya Mani, Ankur Moitra |
| 2023 | ICLR | Distilling Model Failures as Directions in Latent Space. | Saachi Jain, Hannah Lawrence, Ankur Moitra, Aleksander Madry |
| 2023 | ICLR | Provably Auditing Ordinary Least Squares in Low Dimensions. | Ankur Moitra, Dhruv Rohatgi |
| 2023 | ICML | Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau |
| 2023 | SODA | Robust Voting Rules from Algorithmic Robust Statistics. | Allen Liu, Ankur Moitra |
| 2023 | STOC | A New Approach to Learning Linear Dynamical Systems. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Morris Yau |
| 2023 | STOC | Planning and Learning in Partially Observable Systems via Filter Stability. | Noah Golowich, Ankur Moitra, Dhruv Rohatgi |
| 2022 | COLT | Can Q-learning be Improved with Advice? | Noah Golowich, Ankur Moitra |
| 2022 | COLT | Learning GMMs with Nearly Optimal Robustness Guarantees. | Allen Liu, Ankur Moitra |
| 2022 | FOCS | Minimax Rates for Robust Community Detection. | Allen Liu, Ankur Moitra |
| 2022 | STOC | Kalman filtering with adversarial corruptions. | Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau |
| 2021 | COLT | Learning to Sample from Censored Markov Random Fields. | Ankur Moitra, Elchanan Mossel, Colin Sandon |
| 2021 | FOCS | Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. | Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau |
| 2021 | STOC | Algorithmic foundations for the diffraction limit. | Sitan Chen, Ankur Moitra |
| 2021 | STOC | Settling the robust learnability of mixtures of Gaussians. | Allen Liu, Ankur Moitra |
| 2020 | COLT | Rigorous Guarantees for Tyler's M-Estimator via Quantum Expansion. | William Cole Franks, Ankur Moitra |
| 2020 | COLT | Better Algorithms for Estimating Non-Parametric Models in Crowd-Sourcing and Rank Aggregation. | Allen Liu, Ankur Moitra |
| 2020 | COLT | Parallels Between Phase Transitions and Circuit Complexity? | Ankur Moitra, Elchanan Mossel, Colin Sandon |
| 2020 | STOC | Efficiently learning structured distributions from untrusted batches. | Sitan Chen, Jerry Li, Ankur Moitra |
| 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 | SODA | Improved Bounds for Randomly Sampling Colorings via Linear Programming. | Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle |
| 2019 | STOC | Learning restricted Boltzmann machines via influence maximization. | Guy Bresler, Frederic Koehler, Ankur Moitra |
| 2019 | STOC | Beyond the low-degree algorithm: mixtures of subcubes and their applications. | Sitan Chen, Ankur Moitra |
| 2019 | STOC | Spectral methods from tensor networks. | Ankur Moitra, Alexander S. Wein |
| 2018 | FOCS | Efficiently Learning Mixtures of Mallows Models. | Allen Liu, Ankur Moitra |
| 2018 | SODA | Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2017 | COLT | Rates of estimation for determinantal point processes. | Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet, John Urschel |
| 2017 | ICML | Being Robust (in High Dimensions) Can Be Practical. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2017 | ICML | Learning Determinantal Point Processes with Moments and Cycles. | John Urschel, Victor-Emmanuel Brunel, Ankur Moitra, Philippe Rigollet |
| 2017 | STOC | Approximate counting, the Lovasz local lemma, and inference in graphical models. | Ankur Moitra |
| 2016 | COLT | Noisy Tensor Completion via the Sum-of-Squares Hierarchy. | Boaz Barak, Ankur Moitra |
| 2016 | FOCS | A Nearly Tight Sum-of-Squares Lower Bound for the Planted Clique Problem. | Boaz Barak, Samuel B. Hopkins, Jonathan A. Kelner, Pravesh Kothari, Ankur Moitra, Aaron Potechin |
| 2016 | FOCS | Robust Estimators in High Dimensions without the Computational Intractability. | Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart |
| 2016 | ICML | Provable Algorithms for Inference in Topic Models. | Sanjeev Arora, Rong Ge, Frederic Koehler, Tengyu Ma, Ankur Moitra |
| 2016 | STOC | How robust are reconstruction thresholds for community detection? | Ankur Moitra, William Perry, Alexander S. Wein |
| 2015 | COLT | Simple, Efficient, and Neural Algorithms for Sparse Coding. | Sanjeev Arora, Rong Ge, Tengyu Ma, Ankur Moitra |
| 2015 | ISSAC | Nonnegative Matrix Factorization: Algorithms, Complexity and Applications. | Ankur Moitra |
| 2015 | STOC | Super-resolution, Extremal Functions and the Condition Number of Vandermonde Matrices. | Ankur Moitra |
| 2014 | COLT | New Algorithms for Learning Incoherent and Overcomplete Dictionaries. | Sanjeev Arora, Rong Ge, Ankur Moitra |
| 2014 | COLT | Open Problem: Tensor Decompositions: Algorithms up to the Uniqueness Threshold? | Aditya Bhaskara, Moses Charikar, Ankur Moitra, Aravindan Vijayaraghavan |
| 2014 | SODA | A Polynomial-time Approximation Scheme for Fault-tolerant Distributed Storage. | Constantinos Daskalakis, Anindya De, Ilias Diakonikolas, Ankur Moitra, Rocco A. Servedio |
| 2014 | STOC | Smoothed analysis of tensor decompositions. | Aditya Bhaskara, Moses Charikar, Ankur Moitra, Aravindan Vijayaraghavan |
| 2013 | COLT | Algorithms and Hardness for Robust Subspace Recovery. | Moritz Hardt, Ankur Moitra |
| 2013 | FOCS | A Polynomial Time Algorithm for Lossy Population Recovery. | Ankur Moitra, Michael E. Saks |
| 2013 | ICML | A Practical Algorithm for Topic Modeling with Provable Guarantees. | Sanjeev Arora, Rong Ge, Yonatan Halpern, David M. Mimno, Ankur Moitra, David A. Sontag, Yichen Wu, Michael Zhu |
| 2013 | SODA | An Almost Optimal Algorithm for Computing Nonnegative Rank. | Ankur Moitra |
| 2013 | STOC | An information complexity approach to extended formulations. | Mark Braverman, Ankur Moitra |
| 2012 | FOCS | Learning Topic Models - Going beyond SVD. | Sanjeev Arora, Rong Ge, Ankur Moitra |
| 2012 | STOC | Nearly complete graphs decomposable into large induced matchings and their applications. | Noga Alon, Ankur Moitra, Benny Sudakov |
| 2012 | STOC | Computing a nonnegative matrix factorization - provably. | Sanjeev Arora, Rong Ge, Ravindran Kannan, Ankur Moitra |
| 2011 | FOCS | Efficient and Explicit Coding for Interactive Communication. | Ran Gelles, Ankur Moitra, Amit Sahai |
| 2011 | SODA | Capacitated Metric Labeling. | Matthew Andrews, Mohammad Taghi Hajiaghayi, Howard J. Karloff, Ankur Moitra |
| 2011 | STOC | Dueling algorithms. | Nicole Immorlica, Adam Tauman Kalai, Brendan Lucier, Ankur Moitra, Andrew Postlewaite, Moshe Tennenholtz |
| 2011 | STOC | Pareto optimal solutions for smoothed analysts. | Ankur Moitra, Ryan O'Donnell |
| 2010 | FOCS | Vertex Sparsifiers and Abstract Rounding Algorithms. | Moses Charikar, Tom Leighton, Shi Li, Ankur Moitra |
| 2010 | FOCS | Settling the Polynomial Learnability of Mixtures of Gaussians. | Ankur Moitra, Gregory Valiant |
| 2010 | STOC | Efficiently learning mixtures of two Gaussians. | Adam Tauman Kalai, Ankur Moitra, Gregory Valiant |
| 2010 | STOC | Extensions and limits to vertex sparsification. | Frank Thomson Leighton, Ankur Moitra |
| 2009 | FOCS | Approximation Algorithms for Multicommodity-Type Problems with Guarantees Independent of the Graph Size. | Ankur Moitra |
| 2008 | FOCS | Some Results on Greedy Embeddings in Metric Spaces. | Ankur Moitra, Tom Leighton |