Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases. - Paul Beame; Jerry Li 001; Sudeepa Roy 001; Dan Suciu (UAI, 2013)

Model Counting of Query Expressions: Limitations of Propositional Methods. - Paul Beame; Jerry Li 001; Sudeepa Roy 001; Dan Suciu (CoRR, 2013)

Counting of Query Expressions: Limitations of Propositional Methods. - Paul Beame; Jerry Li 001; Sudeepa Roy 001; Dan Suciu (ICDT, 2014)

On the Importance of Registers for Computability. - Rati Gelashvili; Mohsen Ghaffari 001; Jerry Li 001; Nir Shavit (OPODIS, 2014)

Replacing Mark Bits with Randomness in Fibonacci Heaps. - Jerry Li 001; John Peebles (ICALP (1), 2015)

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms. - Jayadev Acharya; Ilias Diakonikolas; Chinmay Hegde; Jerry Zheng Li; Ludwig Schmidt (PODS, 2015)

The SprayList: a scalable relaxed priority queue. - Dan Alistarh; Justin Kopinsky; Jerry Li 001; Nir Shavit (PPoPP, 2015)

A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k. - Jerry Zheng Li; Ludwig Schmidt (CoRR, 2015)

Fast Algorithms for Segmented Regression. - Jayadev Acharya; Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (ICML, 2016)

QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent. - Dan Alistarh; Jerry Li 001; Ryota Tomioka; Milan Vojnovic (CoRR, 2016)

ZipML: An End-to-end Bitwise Framework for Dense Generalized Linear Models. - Hantian Zhang; Kaan Kara; Jerry Li 001; Dan Alistarh; Ji Liu 002; Ce Zhang 001 (CoRR, 2016)

Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities. - Jerry Li 001; Ludwig Schmidt (COLT, 2017)

Computationally Efficient Robust Sparse Estimation in High Dimensions. - Sivaraman Balakrishnan; Simon S. Du; Jerry Li 001; Aarti Singh (COLT, 2017)

Being Robust (in High Dimensions) Can Be Practical. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (ICML, 2017)

ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. - Hantian Zhang; Jerry Li 001; Kaan Kara; Dan Alistarh; Ji Liu 002; Ce Zhang 001 (ICML, 2017)

QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding. - Dan Alistarh; Demjan Grubic; Jerry Li 001; Ryota Tomioka; Milan Vojnovic (NIPS, 2017)

Communication-Efficient Distributed Learning of Discrete Distributions. - Ilias Diakonikolas; Elena Grigorescu; Jerry Li 001; Abhiram Natarajan; Krzysztof Onak; Ludwig Schmidt (NIPS, 2017)

The Power of Choice in Priority Scheduling. - Dan Alistarh; Justin Kopinsky; Jerry Li 001; Giorgi Nadiradze (PODC, 2017)

Sample-Optimal Density Estimation in Nearly-Linear Time. - Jayadev Acharya; Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (SODA, 2017)

Towards Understanding the Dynamics of Generative Adversarial Networks. - Jerry Li 001; Aleksander Madry; John Peebles; Ludwig Schmidt (CoRR, 2017)

Robust Sparse Estimation Tasks in High Dimensions. - Jerry Li 001 (CoRR, 2017)

Exact Model Counting of Query Expressions: Limitations of Propositional Methods. - Paul Beame; Jerry Li 001; Sudeepa Roy 001; Dan Suciu (ACM Trans. Database Syst., 2017)

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. - Ilias Diakonikolas; Jerry Li 001; Ludwig Schmidt (COLT, 2018)

On the Limitations of First-Order Approximation in GAN Dynamics. - Jerry Li 001; Aleksander Madry; John Peebles; Ludwig Schmidt (ICML, 2018)

Byzantine Stochastic Gradient Descent. - Dan Alistarh; Zeyuan Allen-Zhu; Jerry Li 001 (NeurIPS, 2018)

Spectral Signatures in Backdoor Attacks. - Brandon Tran; Jerry Li 001; Aleksander Madry (NeurIPS, 2018)

Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (SODA, 2018)

Distributionally Linearizable Data Structures. - Dan Alistarh; Trevor Brown 001; Justin Kopinsky; Jerry Zheng Li; Giorgi Nadiradze (SPAA, 2018)

Mixture models, robustness, and sum of squares proofs. - Samuel B. Hopkins 001; Jerry Li 001 (STOC, 2018)

Principled approaches to robust machine learning and beyond. - Jerry Zheng Li (2018)

How Hard is Robust Mean Estimation? - Samuel B. Hopkins 001; Jerry Li 001 (COLT, 2019)

Privately Learning High-Dimensional Distributions. - Gautam Kamath 001; Jerry Li 001; Vikrant Singhal; Jonathan R. Ullman (COLT, 2019)

On Mean Estimation for General Norms with Statistical Queries. - Jerry Li 001; Aleksandar Nikolov; Ilya P. Razenshteyn; Erik Waingarten (COLT, 2019)

Sever: A Robust Meta-Algorithm for Stochastic Optimization. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Jacob Steinhardt; Alistair Stewart (ICML, 2019)

Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. - Yihe Dong; Samuel B. Hopkins 001; Jerry Li 001 (NeurIPS, 2019)

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. - Hadi Salman; Jerry Li 001; Ilya P. Razenshteyn; Pengchuan Zhang; Huan Zhang; Sébastien Bubeck; Greg Yang (NeurIPS, 2019)

Finding the Mode of a Kernel Density Estimate. - Jasper C. H. Lee; Jerry Li 001; Christopher Musco; Jeff M. Phillips; Wai Ming Tai (CoRR, 2019)

Robust Estimators in High-Dimensions Without the Computational Intractability. - Ilias Diakonikolas; Gautam Kamath 001; Daniel Kane 001; Jerry Li 001; Ankur Moitra; Alistair Stewart (SIAM J. Comput., 2019)

Entanglement is Necessary for Optimal Quantum Property Testing. - Sébastien Bubeck; Sitan Chen; Jerry Li 001 (FOCS, 2020)

Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers. - Hannah Lawrence; Jerry Li 001; Cameron Musco; Christopher Musco (ICASSP, 2020)

Randomized Smoothing of All Shapes and Sizes. - Greg Yang; Tony Duan; J. Edward Hu; Hadi Salman; Ilya P. Razenshteyn; Jerry Li 001 (ICML, 2020)

Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time. - Jerry Li 001; Guanghao Ye (NeurIPS, 2020)

Learning Structured Distributions From Untrusted Batches: Faster and Simpler. - Sitan Chen; Jerry Li 001; Ankur Moitra (NeurIPS, 2020)

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization. - Samuel B. Hopkins 001; Jerry Li 001; Fred Zhang (NeurIPS, 2020)

Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. - Arun Jambulapati; Jerry Li 001; Kevin Tian (NeurIPS, 2020)

Sample Efficient Toeplitz Covariance Estimation. - Yonina C. Eldar; Jerry Li 001; Cameron Musco; Christopher Musco (SODA, 2020)

Efficiently learning structured distributions from untrusted batches. - Sitan Chen; Jerry Li 001; Ankur Moitra (STOC, 2020)

Learning mixtures of linear regressions in subexponential time via Fourier moments. - Sitan Chen; Jerry Li 001; Zhao Song 002 (STOC, 2020)

Positive semidefinite programming: mixed, parallel, and width-independent. - Arun Jambulapati; Yin Tat Lee; Jerry Li 001; Swati Padmanabhan; Kevin Tian (STOC, 2020)

Efficient Algorithms for Multidimensional Segmented Regression. - Ilias Diakonikolas; Jerry Li 001; Anastasia Voloshinov (CoRR, 2020)

Security and Machine Learning in the Real World. - Ivan Evtimov; Weidong Cui; Ece Kamar; Emre Kiciman; Tadayoshi Kohno; Jerry Li 001 (CoRR, 2020)

Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise. - Jerry Li 001; Aaron Sidford; Kevin Tian; Huishuai Zhang (CoRR, 2020)

Statistical Query Algorithms and Low Degree Tests Are Almost Equivalent. - Matthew S. Brennan; Guy Bresler; Samuel B. Hopkins 001; Jerry Li 001; Tselil Schramm (COLT, 2021)

Finding an Approximate Mode of a Kernel Density Estimate. - Jasper C. H. Lee; Jerry Li 001; Christopher Musco; Jeff M. Phillips; Wai Ming Tai (ESA, 2021)

Exponential Separations Between Learning With and Without Quantum Memory. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (FOCS, 2021)

Byzantine-Resilient Non-Convex Stochastic Gradient Descent. - Zeyuan Allen-Zhu; Faeze Ebrahimianghazani; Jerry Li 001; Dan Alistarh (ICLR, 2021)

Aligning AI With Shared Human Values. - Dan Hendrycks; Collin Burns; Steven Basart; Andrew Critch; Jerry Li 001; Dawn Song; Jacob Steinhardt (ICLR, 2021)

List-Decodable Mean Estimation in Nearly-PCA Time. - Ilias Diakonikolas; Daniel Kane 001; Daniel Kongsgaard; Jerry Li 001; Kevin Tian (NeurIPS, 2021)

Robust Regression Revisited: Acceleration and Improved Estimation Rates. - Arun Jambulapati; Jerry Li 001; Tselil Schramm; Kevin Tian (NeurIPS, 2021)

Robustness meets algorithms. - Ilias Diakonikolas; Gautam Kamath 001; Daniel M. Kane; Jerry Li 001; Ankur Moitra; Alistair Stewart (Commun. ACM, 2021)

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

A Hierarchy for Replica Quantum Advantage. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (CoRR, 2021)

Quantum advantage in learning from experiments. - Hsin-Yuan Huang; Michael Broughton; Jordan Cotler; Sitan Chen; Jerry Li 001; Masoud Mohseni; Hartmut Neven; Ryan Babbush; Richard Kueng; John Preskill; Jarrod R. McClean (CoRR, 2021)

On Distinctive Properties of Universal Perturbations. - Sung Min Park; Kuo-An Wei; Kai Yuanqing Xiao; Jerry Li 001; Aleksander Madry (CoRR, 2021)

The Price of Tolerance in Distribution Testing. - Clément L. Canonne; Ayush Jain 001; Gautam Kamath 001; Jerry Li 001 (COLT, 2022)

Toward Instance-Optimal State Certification With Incoherent Measurements. - Sitan Chen; Jerry Li 001; Ryan O'Donnell (COLT, 2022)

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

Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. - Sitan Chen; Jerry Li 001; Yuanzhi Li; Raghu Meka (ICLR, 2022)

Learning (Very) Simple Generative Models Is Hard. - Sitan Chen; Jerry Li 001; Yuanzhi Li (NeurIPS, 2022)

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

Clustering mixture models in almost-linear time via list-decodable mean estimation. - Ilias Diakonikolas; Daniel M. Kane; Daniel Kongsgaard; Jerry Li 001; Kevin Tian (STOC, 2022)

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

Learning Polynomial Transformations. - Sitan Chen; Jerry Li 001; Yuanzhi Li; Anru R. Zhang (CoRR, 2022)

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

The Complexity of NISQ. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (CoRR, 2022)

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

Automatic Prompt Optimization with "Gradient Descent" and Beam Search. - Reid Pryzant; Dan Iter; Jerry Li 001; Yin Tat Lee; Chenguang Zhu 001; Michael Zeng 001 (EMNLP, 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)

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. - Sitan Chen; Sinho Chewi; Jerry Li 001; Yuanzhi Li; Adil Salim; Anru Zhang (ICLR, 2023)

Structured Semidefinite Programming for Recovering Structured Preconditioners. - Arun Jambulapati; Jerry Li 001; Christopher Musco; Kirankumar Shiragur; Aaron Sidford; Kevin Tian (NeurIPS, 2023)

Learning Polynomial Transformations via Generalized Tensor Decompositions. - Sitan Chen; Jerry Li 001; Yuanzhi Li; Anru R. Zhang (STOC, 2023)

Black-Box k-to-1-PCA Reductions: Theory and Applications. - Arun Jambulapati; Syamantak Kumar; Jerry Li 001; Shourya Pandey; Ankit Pensia; Kevin Tian (COLT, 2024)

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

Query Lower Bounds for Log-concave Sampling. - Sinho Chewi; Jaume de Dios Pont; Jerry Li 001; Chen Lu 002; Shyam Narayanan (J. ACM, 2024)

Predicting quantum channels over general product distributions. - Sitan Chen; Jaume de Dios Pont; Jun-Ting Hsieh; Hsin-Yuan Huang; Jane Lange; Jerry Li 001 (COLT, 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)

S4S: Solving for a Diffusion Model Solver. - Eric Frankel; Sitan Chen; Jerry Li 001; Pang Wei Koh; Lillian J. Ratliff; Sewoong Oh (CoRR, 2025)