| 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 |
| 2025 | ICALP | Near-Optimal Trace Reconstruction for Mildly Separated Strings. | Anders Aamand, Allen Liu, Shyam Narayanan |
| 2025 | STOC | Learning the Closest Product State. | Ainesh Bakshi, John Bostanci, William Kretschmer, Zeph Landau, Jerry Li, Allen Liu, Ryan O'Donnell, Ewin Tang |
| 2025 | STOC | Model Stealing for Any Low-Rank Language Model. | Allen Liu, 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 | STOC | Learning Quantum Hamiltonians at Any Temperature in Polynomial Time. | Ainesh Bakshi, Allen Liu, Ankur Moitra, Ewin Tang |
| 2024 | STOC | An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography. | Sitan Chen, Jerry Li, Allen Liu |
| 2023 | COLT | Semi-Random Sparse Recovery in Nearly-Linear Time. | Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian |
| 2023 | FOCS | The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination. | Clment L. Canonne, Samuel B. Hopkins, Jerry Li, Allen Liu, Shyam Narayanan |
| 2023 | FOCS | When Does Adaptivity Help for Quantum State Learning? | Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke |
| 2023 | FOCS | Matrix Completion in Almost-Verification Time. | Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian |
| 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 |
| 2022 | COLT | Learning GMMs with Nearly Optimal Robustness Guarantees. | Allen Liu, Ankur Moitra |
| 2022 | COLT | The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication. | Allen Liu, Mark Sellke |
| 2022 | FOCS | Tight Bounds for Quantum State Certification with Incoherent Measurements. | Sitan Chen, Jerry Li, Brice Huang, Allen Liu |
| 2022 | FOCS | Minimax Rates for Robust Community Detection. | Allen Liu, Ankur Moitra |
| 2022 | STOC | Clustering mixtures with almost optimal separation in polynomial time. | Allen Liu, Jerry Li |
| 2021 | SODA | Optimal Contextual Pricing and Extensions. | Allen Liu, Renato Paes Leme, Jon Schneider |
| 2021 | STOC | Settling the robust learnability of mixtures of Gaussians. | Allen Liu, Ankur Moitra |
| 2020 | COLT | Better Algorithms for Estimating Non-Parametric Models in Crowd-Sourcing and Rank Aggregation. | Allen Liu, Ankur Moitra |
| 2018 | FOCS | Efficiently Learning Mixtures of Mallows Models. | Allen Liu, Ankur Moitra |