Efficiently Learning Mixtures of Mallows Models. - Allen Liu; Ankur Moitra (FOCS, 2018)

Fourier and Circulant Matrices are Not Rigid. - Zeev Dvir; Allen Liu (Electron. Colloquium Comput. Complex., 2019)

Better Algorithms for Estimating Non-Parametric Models in Crowd-Sourcing and Rank Aggregation. - Allen Liu; Ankur Moitra (COLT, 2020)

Myersonian Regression. - Allen Liu; Renato Paes Leme; Jon Schneider (NeurIPS, 2020)

Tensor Completion Made Practical. - Allen Liu; Ankur Moitra (NeurIPS, 2020)

Contextual Search for General Hypothesis Classes. - Allen Liu; Renato Paes Leme; Jon Schneider (CoRR, 2020)

Competing Optimally Against An Imperfect Prophet. - Allen Liu; Renato Paes Leme; Martin Pal; Jon Schneider; Balasubramanian Sivan (CoRR, 2020)

Distributed Load Balancing: A New Framework and Improved Guarantees. - Sara Ahmadian; Allen Liu; Binghui Peng; Morteza Zadimoghaddam (ITCS, 2021)

Margin-Independent Online Multiclass Learning via Convex Geometry. - Guru Guruganesh; Allen Liu; Jon Schneider; Joshua R. Wang (NeurIPS, 2021)

Variable Decomposition for Prophet Inequalities and Optimal Ordering. - Allen Liu; Renato Paes Leme; Martin Pál; Jon Schneider; Balasubramanian Sivan (EC, 2021)

Optimal Contextual Pricing and Extensions. - Allen Liu; Renato Paes Leme; Jon Schneider (SODA, 2021)

Settling the robust learnability of mixtures of Gaussians. - Allen Liu; Ankur Moitra (STOC, 2021)

How to Decompose a Tensor with Group Structure. - Allen Liu; Ankur Moitra (CoRR, 2021)

Sparsification for Sums of Exponentials and its Algorithmic Applications. - Jerry Li 001; Allen Liu; Ankur Moitra (CoRR, 2021)

Learning GMMs with Nearly Optimal Robustness Guarantees. - Allen Liu; Ankur Moitra (COLT, 2022)

The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communication. - Allen Liu; Mark Sellke (COLT, 2022)

Tight Bounds for Quantum State Certification with Incoherent Measurements. - Sitan Chen; Jerry Li 001; Brice Huang; Allen Liu (FOCS, 2022)

Minimax Rates for Robust Community Detection. - Allen Liu; Ankur Moitra (FOCS, 2022)

Robust Model Selection and Nearly-Proper Learning for GMMs. - Allen Liu; Jerry Li 001; Ankur Moitra (NeurIPS, 2022)

Clustering mixtures with almost optimal separation in polynomial time. - Allen Liu; Jerry Li 001 (STOC, 2022)

Tight Bounds for State Tomography with Incoherent Measurements. - Sitan Chen; Brice Huang; Jerry Li 001; Allen Liu; Mark Sellke (CoRR, 2022)

Semi-Random Sparse Recovery in Nearly-Linear Time. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (COLT, 2023)

The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination. - Clément L. Canonne; Samuel B. Hopkins 001; Jerry Li 001; Allen Liu; Shyam Narayanan (FOCS, 2023)

When Does Adaptivity Help for Quantum State Learning? - Sitan Chen; Brice Huang; Jerry Li 001; Allen Liu; Mark Sellke (FOCS, 2023)

Matrix Completion in Almost-Verification Time. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (FOCS, 2023)

Tensor Decompositions Meet Control Theory: Learning General Mixtures of Linear Dynamical Systems. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Morris Yau (ICML, 2023)

Constant Approximation for Individual Preference Stable Clustering. - Anders Aamand; Justin Y. Chen; Allen Liu; Sandeep Silwal; Pattara Sukprasert; Ali Vakilian; Fred Zhang (NeurIPS, 2023)

Robust Voting Rules from Algorithmic Robust Statistics. - Allen Liu; Ankur Moitra (SODA, 2023)

A New Approach to Learning Linear Dynamical Systems. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Morris Yau (STOC, 2023)

Robustly Learning General Mixtures of Gaussians. - Allen Liu; Ankur Moitra (J. ACM, 2023)

High-Temperature Gibbs States are Unentangled and Efficiently Preparable. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Ewin Tang (FOCS, 2024)

Structure Learning of Hamiltonians from Real-Time Evolution. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Ewin Tang (FOCS, 2024)

Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting. - Jonathan A. Kelner; Jerry Li 001; Allen Liu; Aaron Sidford; Kevin Tian (NeurIPS, 2024)

Learning Quantum Hamiltonians at Any Temperature in Polynomial Time. - Ainesh Bakshi; Allen Liu; Ankur Moitra; Ewin Tang (STOC, 2024)

An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography. - Sitan Chen; Jerry Li 001; Allen Liu (STOC, 2024)

Optimal high-precision shadow estimation. - Sitan Chen; Jerry Li 001; Allen Liu (CoRR, 2024)

Near-Optimal Trace Reconstruction for Mildly Separated Strings. - Anders Aamand; Allen Liu; Shyam Narayanan (ICALP, 2025)

Learning the Closest Product State. - Ainesh Bakshi; John Bostanci; William Kretschmer; Zeph Landau; Jerry Li 001; Allen Liu; Ryan O'Donnell; Ewin Tang (STOC, 2025)

Model Stealing for Any Low-Rank Language Model. - Allen Liu; Ankur Moitra (STOC, 2025)

Are LLMs complicated ethical dilemma analyzers? - Jiashen Du; Jesse Yao; Allen Liu; Zhekai Zhang (CoRR, 2025)