Jerry Li - RankMe

142 papers A* 59A 3B 2Misc 1Journal 74Unranked 3

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Rigorous Implications of the Low-Degree Heuristic. CoRR Jun-Ting Hsieh, Daniel M. Kane, Pravesh K. Kothari, Jerry Li, Sidhanth Mohanty, Stefan Tiegel
2025	A*	`conf`	Learning the Closest Product State. STOC Ainesh Bakshi, John Bostanci, William Kretschmer, Zeph Landau, Jerry Li, Allen Liu, Ryan O'Donnell, Ewin Tang
2025	A*	`conf`	Predicting quantum channels over general product distributions. COLT Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li
2025	J	`jnl`	S4S: Solving for a Diffusion Model Solver. CoRR Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh
2025	A*	`conf`	S4S: Solving for a Fast Diffusion Model Solver. ICML Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh
2024	A*	`conf`	An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography. STOC Sitan Chen, Jerry Li, Allen Liu
2024	J	`jnl`	An optimal tradeoff between entanglement and copy complexity for state tomography. CoRR Sitan Chen, Jerry Li, Allen Liu
2024	A*	`conf`	Black-Box k-to-1-PCA Reductions: Theory and Applications. COLT Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian
2024	J	`jnl`	Black-Box k-to-1-PCA Reductions: Theory and Applications. CoRR Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian
2024	J	`jnl`	Optimal high-precision shadow estimation. CoRR Sitan Chen, Jerry Li, Allen Liu
2024	J	`jnl`	Predicting quantum channels over general product distributions. CoRR Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li
2024	J	`jnl`	Query Lower Bounds for Log-concave Sampling. J. ACM Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan
2024	A*	`conf`	Semi-Random Matrix Completion via Flow-Based Adaptive Reweighting. NeurIPS Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
2023	A*	`conf`	Automatic Prompt Optimization with "Gradient Descent" and Beam Search. EMNLP Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng
2023	J	`jnl`	Automatic Prompt Optimization with "Gradient Descent" and Beam Search. CoRR Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng
2023	A*	`conf`	Learning Polynomial Transformations via Generalized Tensor Decompositions. STOC Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang
2023	A*	`conf`	Matrix Completion in Almost-Verification Time. FOCS Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
2023	J	`jnl`	Matrix Completion in Almost-Verification Time. CoRR Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
2023	A*	`conf`	Query lower bounds for log-concave sampling. FOCS Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan
2023	J	`jnl`	Query lower bounds for log-concave sampling. CoRR Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan
2023	A*	`conf`	Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. ICLR Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang
2023	A*	`conf`	Semi-Random Sparse Recovery in Nearly-Linear Time. COLT Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
2023	A*	`conf`	Structured Semidefinite Programming for Recovering Structured Preconditioners. NeurIPS Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian
2023	J	`jnl`	Structured Semidefinite Programming for Recovering Structured Preconditioners. CoRR Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian
2023	A*	`conf`	The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination. FOCS Clément L. Canonne, Samuel B. Hopkins, Jerry Li, Allen Liu, Shyam Narayanan
2023	J	`jnl`	The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination. CoRR Clément L. Canonne, Samuel B. Hopkins, Jerry Li, Allen Liu, Shyam Narayanan
2023	A*	`conf`	When Does Adaptivity Help for Quantum State Learning? FOCS Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke
2022	A*	`conf`	Clustering mixture models in almost-linear time via list-decodable mean estimation. STOC Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
2022	A*	`conf`	Clustering mixtures with almost optimal separation in polynomial time. STOC Allen Liu, Jerry Li
2022	A*	`conf`	Learning (Very) Simple Generative Models Is Hard. NeurIPS Sitan Chen, Jerry Li, Yuanzhi Li
2022	J	`jnl`	Learning (Very) Simple Generative Models Is Hard. CoRR Sitan Chen, Jerry Li, Yuanzhi Li
2022	J	`jnl`	Learning Polynomial Transformations. CoRR Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang
2022	A*	`conf`	Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. ICLR Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
2022	J	`jnl`	Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. CoRR Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
2022	A*	`conf`	Robust Model Selection and Nearly-Proper Learning for GMMs. NeurIPS Allen Liu, Jerry Li, Ankur Moitra
2022	J	`jnl`	Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. CoRR Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang
2022	J	`jnl`	Semi-Random Sparse Recovery in Nearly-Linear Time. CoRR Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian
2022	J	`jnl`	The Complexity of NISQ. CoRR Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
2022	A*	`conf`	The Price of Tolerance in Distribution Testing. COLT Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li
2022	A*	`conf`	Tight Bounds for Quantum State Certification with Incoherent Measurements. FOCS Sitan Chen, Jerry Li, Brice Huang, Allen Liu
2022	J	`jnl`	Tight Bounds for Quantum State Certification with Incoherent Measurements. CoRR Sitan Chen, Brice Huang, Jerry Li, Allen Liu
2022	J	`jnl`	Tight Bounds for State Tomography with Incoherent Measurements. CoRR Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke
2022	A*	`conf`	Toward Instance-Optimal State Certification With Incoherent Measurements. COLT Sitan Chen, Jerry Li, Ryan O'Donnell
2021	J	`jnl`	A Hierarchy for Replica Quantum Advantage. CoRR Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
2021	A*	`conf`	Aligning AI With Shared Human Values. ICLR Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt
2021	A*	`conf`	Byzantine-Resilient Non-Convex Stochastic Gradient Descent. ICLR Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh
2021	J	`jnl`	Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation. CoRR Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
2021	J	`jnl`	Clustering Mixtures with Almost Optimal Separation in Polynomial Time. CoRR Jerry Li, Allen Liu
2021	A*	`conf`	Exponential Separations Between Learning With and Without Quantum Memory. FOCS Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
2021	J	`jnl`	Exponential separations between learning with and without quantum memory. CoRR Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
2021	A	`conf`	Finding an Approximate Mode of a Kernel Density Estimate. ESA Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai
2021	A*	`conf`	List-Decodable Mean Estimation in Nearly-PCA Time. NeurIPS Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
2021	J	`jnl`	On Distinctive Properties of Universal Perturbations. CoRR Sung Min Park, Kuo-An Wei, Kai Yuanqing Xiao, Jerry Li, Aleksander Madry
2021	J	`jnl`	Quantum advantage in learning from experiments. CoRR Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean
2021	A*	`conf`	Robust Regression Revisited: Acceleration and Improved Estimation Rates. NeurIPS Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian
2021	J	`jnl`	Robust Regression Revisited: Acceleration and Improved Estimation Rates. CoRR Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian
2021	J	`jnl`	Robustness meets algorithms. Commun. ACM Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2021	J	`jnl`	Sparsification for Sums of Exponentials and its Algorithmic Applications. CoRR Jerry Li, Allen Liu, Ankur Moitra
2021	A*	`conf`	Statistical Query Algorithms and Low Degree Tests Are Almost Equivalent. COLT Matthew S. Brennan, Guy Bresler, Samuel B. Hopkins, Jerry Li, Tselil Schramm
2021	J	`jnl`	The Price of Tolerance in Distribution Testing. CoRR Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li
2021	J	`jnl`	Toward Instance-Optimal State Certification With Incoherent Measurements. CoRR Sitan Chen, Jerry Li, Ryan O'Donnell
2020	J	`jnl`	Aligning AI With Shared Human Values. CoRR Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt
2020	J	`jnl`	Byzantine-Resilient Non-Convex Stochastic Gradient Descent. CoRR Zeyuan Allen-Zhu, Faeze Ebrahimian, Jerry Li, Dan Alistarh
2020	J	`jnl`	Efficient Algorithms for Multidimensional Segmented Regression. CoRR Ilias Diakonikolas, Jerry Li, Anastasia Voloshinov
2020	A*	`conf`	Efficiently learning structured distributions from untrusted batches. STOC Sitan Chen, Jerry Li, Ankur Moitra
2020	A*	`conf`	Entanglement is Necessary for Optimal Quantum Property Testing. FOCS Sébastien Bubeck, Sitan Chen, Jerry Li
2020	J	`jnl`	Entanglement is Necessary for Optimal Quantum Property Testing. CoRR Sébastien Bubeck, Sitan Chen, Jerry Li
2020	A*	`conf`	Learning Structured Distributions From Untrusted Batches: Faster and Simpler. NeurIPS Sitan Chen, Jerry Li, Ankur Moitra
2020	J	`jnl`	Learning Structured Distributions From Untrusted Batches: Faster and Simpler. CoRR Sitan Chen, Jerry Li, Ankur Moitra
2020	A*	`conf`	Learning mixtures of linear regressions in subexponential time via Fourier moments. STOC Sitan Chen, Jerry Li, Zhao Song
2020	J	`jnl`	List-Decodable Mean Estimation in Nearly-PCA Time. CoRR Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
2020	Misc	`conf`	Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers. ICASSP Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco
2020	J	`jnl`	Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent. CoRR Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian
2020	A*	`conf`	Positive semidefinite programming: mixed, parallel, and width-independent. STOC Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian
2020	A*	`conf`	Randomized Smoothing of All Shapes and Sizes. ICML Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya P. Razenshteyn, Jerry Li
2020	J	`jnl`	Randomized Smoothing of All Shapes and Sizes. CoRR Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya P. Razenshteyn, Jerry Li
2020	A*	`conf`	Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time. NeurIPS Jerry Li, Guanghao Ye
2020	J	`jnl`	Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time. CoRR Jerry Li, Guanghao Ye
2020	A*	`conf`	Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. NeurIPS Arun Jambulapati, Jerry Li, Kevin Tian
2020	J	`jnl`	Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. CoRR Arun Jambulapati, Jerry Li, Kevin Tian
2020	A*	`conf`	Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization. NeurIPS Samuel B. Hopkins, Jerry Li, Fred Zhang
2020	J	`jnl`	Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization. CoRR Samuel B. Hopkins, Jerry Li, Fred Zhang
2020	A*	`conf`	Sample Efficient Toeplitz Covariance Estimation. SODA Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco
2020	J	`jnl`	Security and Machine Learning in the Real World. CoRR Ivan Evtimov, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, Jerry Li
2020	J	`jnl`	Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent. CoRR Matthew S. Brennan, Guy Bresler, Samuel B. Hopkins, Jerry Li, Tselil Schramm
2020	J	`jnl`	Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise. CoRR Jerry Li, Aaron Sidford, Kevin Tian, Huishuai Zhang
2019	J	`jnl`	Efficiently Learning Structured Distributions from Untrusted Batches. CoRR Sitan Chen, Jerry Li, Ankur Moitra
2019	J	`jnl`	Finding the Mode of a Kernel Density Estimate. CoRR Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai
2019	J	`jnl`	How Hard Is Robust Mean Estimation? CoRR Samuel B. Hopkins, Jerry Li
2019	A*	`conf`	How Hard is Robust Mean Estimation? COLT Samuel B. Hopkins, Jerry Li
2019	J	`jnl`	Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments. CoRR Sitan Chen, Jerry Li, Zhao Song
2019	J	`jnl`	Low-Rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers. CoRR Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco
2019	A*	`conf`	On Mean Estimation for General Norms with Statistical Queries. COLT Jerry Li, Aleksandar Nikolov, Ilya P. Razenshteyn, Erik Waingarten
2019	J	`jnl`	On Mean Estimation for General Norms with Statistical Queries. CoRR Jerry Li, Aleksandar Nikolov, Ilya P. Razenshteyn, Erik Waingarten
2019	A*	`conf`	Privately Learning High-Dimensional Distributions. COLT Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
2019	A*	`conf`	Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. NeurIPS Hadi Salman, Jerry Li, Ilya P. Razenshteyn, Pengchuan Zhang, Huan Zhang, Sébastien Bubeck, Greg Yang
2019	J	`jnl`	Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. CoRR Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya P. Razenshteyn, Sébastien Bubeck
2019	A*	`conf`	Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. NeurIPS Yihe Dong, Samuel B. Hopkins, Jerry Li
2019	J	`jnl`	Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. CoRR Yihe Dong, Samuel B. Hopkins, Jerry Li
2019	J	`jnl`	Robust Estimators in High-Dimensions Without the Computational Intractability. SIAM J. Comput. Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2019	J	`jnl`	Sample Efficient Toeplitz Covariance Estimation. CoRR Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco
2019	A*	`conf`	Sever: A Robust Meta-Algorithm for Stochastic Optimization. ICML Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
2018	A*	`conf`	Byzantine Stochastic Gradient Descent. NeurIPS Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
2018	J	`jnl`	Byzantine Stochastic Gradient Descent. CoRR Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
2018	A*	`conf`	Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. COLT Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
2018	J	`jnl`	Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. CoRR Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
2018	A*	`conf`	Mixture models, robustness, and sum of squares proofs. STOC Samuel B. Hopkins, Jerry Li
2018	A*	`conf`	On the Limitations of First-Order Approximation in GAN Dynamics. ICML Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt
2018	J	`jnl`	Privately Learning High-Dimensional Distributions. CoRR Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman
2018	A*	`conf`	Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. SODA Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2018	J	`jnl`	Sever: A Robust Meta-Algorithm for Stochastic Optimization. CoRR Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
2018	A*	`conf`	Spectral Signatures in Backdoor Attacks. NeurIPS Brandon Tran, Jerry Li, Aleksander Madry
2018	J	`jnl`	Spectral Signatures in Backdoor Attacks. CoRR Brandon Tran, Jerry Li, Aleksander Madry
2017	A*	`conf`	Being Robust (in High Dimensions) Can Be Practical. ICML Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2017	J	`jnl`	Being Robust (in High Dimensions) Can Be Practical. CoRR Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2017	—	`conf`	Communication-Efficient Distributed Learning of Discrete Distributions. NIPS Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt
2017	A*	`conf`	Computationally Efficient Robust Sparse Estimation in High Dimensions. COLT Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh
2017	J	`jnl`	Exact Model Counting of Query Expressions: Limitations of Propositional Methods. ACM Trans. Database Syst. Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu
2017	J	`jnl`	Mixture Models, Robustness, and Sum of Squares Proofs. CoRR Samuel B. Hopkins, Jerry Li
2017	—	`conf`	QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding. NIPS Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic
2017	J	`jnl`	Robust Sparse Estimation Tasks in High Dimensions. CoRR Jerry Li
2017	A*	`conf`	Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities. COLT Jerry Li, Ludwig Schmidt
2017	J	`jnl`	Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. CoRR Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2017	A*	`conf`	Sample-Optimal Density Estimation in Nearly-Linear Time. SODA Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
2017	A*	`conf`	The Power of Choice in Priority Scheduling. PODC Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze
2017	J	`jnl`	The Power of Choice in Priority Scheduling. CoRR Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze
2017	J	`jnl`	Towards Understanding the Dynamics of Generative Adversarial Networks. CoRR Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt
2017	A*	`conf`	ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. ICML Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang
2016	A*	`conf`	Fast Algorithms for Segmented Regression. ICML Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
2016	J	`jnl`	Fast Algorithms for Segmented Regression. CoRR Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
2016	J	`jnl`	QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent. CoRR Dan Alistarh, Jerry Li, Ryota Tomioka, Milan Vojnovic
2016	A*	`conf`	Robust Estimators in High Dimensions without the Computational Intractability. FOCS Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart
2016	J	`jnl`	ZipML: An End-to-end Bitwise Framework for Dense Generalized Linear Models. CoRR Hantian Zhang, Kaan Kara, Jerry Li, Dan Alistarh, Ji Liu, Ce Zhang
2015	—	`conf`	Replacing Mark Bits with Randomness in Fibonacci Heaps. ICALP (1) Jerry Li, John Peebles
2015	B	`conf`	The SprayList: a scalable relaxed priority queue. PPoPP Dan Alistarh, Justin Kopinsky, Jerry Li, Nir Shavit
2014	A	`conf`	Counting of Query Expressions: Limitations of Propositional Methods. ICDT Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu
2014	B	`conf`	On the Importance of Registers for Computability. OPODIS Rati Gelashvili, Mohsen Ghaffari, Jerry Li, Nir Shavit
2014	J	`jnl`	On the Importance of Registers for Computability. CoRR Rati Gelashvili, Mohsen Ghaffari, Jerry Li, Nir Shavit
2014	J	`jnl`	Replacing Mark Bits with Randomness in Fibonacci Heaps. CoRR Jerry Li, John Peebles
2013	A	`conf`	Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases. UAI Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu
2013	J	`jnl`	Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases. CoRR Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu
2013	J	`jnl`	Model Counting of Query Expressions: Limitations of Propositional Methods. CoRR Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu