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ORCIDJerry Li 0001
Jerry Zheng Li
Person information
- affiliation: Microsoft Research AI
- affiliation (former): Massachusetts Institute of Technology (MIT)
Other persons with the same name
- Jerry Li — disambiguation page
- Jerry Li 0002 — Snap Corporation, Seattle, WA, USA
- Jerry Li 0003 — Northwestern University Evanston, IL, USA
- Jerry Li 0004
![[0000-0002-8774-826X]](https://dblp.dagstuhl.de/img/orcid-mark.12x12.png "0000-0002-8774-826X")
— Torrey Pines High School, San Diego, CA, USA
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Books and Theses
- 2018
[b1]Jerry Zheng Li:
Principled approaches to robust machine learning and beyond. Massachusetts Institute of Technology, Cambridge, USA, 2018
Journal Articles
- 2021
- [j3]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustness meets algorithms. Commun. ACM 64(5): 107-115 (2021) - 2019
- [j2]Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High-Dimensions Without the Computational Intractability. SIAM J. Comput. 48(2): 742-864 (2019) - 2017
- [j1]Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu:
Exact Model Counting of Query Expressions: Limitations of Propositional Methods. ACM Trans. Database Syst. 42(1): 1:1-1:46 (2017)
Conference and Workshop Papers
- 2023
- [c64]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Semi-Random Sparse Recovery in Nearly-Linear Time. COLT 2023: 2352-2398 - [c63]Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng:
Automatic Prompt Optimization with "Gradient Descent" and Beam Search. EMNLP 2023: 7957-7968 - [c62]Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke:
When Does Adaptivity Help for Quantum State Learning? FOCS 2023: 391-404 - [c61]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Matrix Completion in Almost-Verification Time. FOCS 2023: 2102-2128 - [c60]Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan:
Query lower bounds for log-concave sampling. FOCS 2023: 2139-2148 - [c59]Clément L. Canonne, Samuel B. Hopkins, Jerry Li, Allen Liu, Shyam Narayanan:
The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination. FOCS 2023: 2159-2168 - [c58]Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang:
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. ICLR 2023 - [c57]Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian:
Structured Semidefinite Programming for Recovering Structured Preconditioners. NeurIPS 2023 - [c56]Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang:
Learning Polynomial Transformations via Generalized Tensor Decompositions. STOC 2023: 1671-1684 - 2022
- [c55]Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li:
The Price of Tolerance in Distribution Testing. COLT 2022: 573-624 - [c54]Sitan Chen, Jerry Li, Ryan O'Donnell:
Toward Instance-Optimal State Certification With Incoherent Measurements. COLT 2022: 2541-2596 - [c53]Sitan Chen, Jerry Li, Brice Huang, Allen Liu:
Tight Bounds for Quantum State Certification with Incoherent Measurements. FOCS 2022: 1205-1213 - [c52]Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka:
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. ICLR 2022 - [c51]Sitan Chen, Jerry Li, Yuanzhi Li:
Learning (Very) Simple Generative Models Is Hard. NeurIPS 2022 - [c50]Allen Liu, Jerry Li, Ankur Moitra:
Robust Model Selection and Nearly-Proper Learning for GMMs. NeurIPS 2022 - [c49]Allen Liu, Jerry Li:
Clustering mixtures with almost optimal separation in polynomial time. STOC 2022: 1248-1261 - [c48]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
Clustering mixture models in almost-linear time via list-decodable mean estimation. STOC 2022: 1262-1275 - 2021
- [c47]Matthew S. Brennan, Guy Bresler, Samuel B. Hopkins, Jerry Li, Tselil Schramm:
Statistical Query Algorithms and Low Degree Tests Are Almost Equivalent. COLT 2021: 774 - [c46]Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai:
Finding an Approximate Mode of a Kernel Density Estimate. ESA 2021: 61:1-61:19 - [c45]Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li:
Exponential Separations Between Learning With and Without Quantum Memory. FOCS 2021: 574-585 - [c44]Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li, Dan Alistarh:
Byzantine-Resilient Non-Convex Stochastic Gradient Descent. ICLR 2021 - [c43]Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt:
Aligning AI With Shared Human Values. ICLR 2021 - [c42]Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian:
Robust Regression Revisited: Acceleration and Improved Estimation Rates. NeurIPS 2021: 4475-4488 - [c41]Ilias Diakonikolas, Daniel Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
List-Decodable Mean Estimation in Nearly-PCA Time. NeurIPS 2021: 10195-10208 - 2020
- [c40]Sébastien Bubeck, Sitan Chen, Jerry Li:
Entanglement is Necessary for Optimal Quantum Property Testing. FOCS 2020: 692-703 - [c39]Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco:
Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers. ICASSP 2020: 4796-4800 - [c38]Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya P. Razenshteyn, Jerry Li:
Randomized Smoothing of All Shapes and Sizes. ICML 2020: 10693-10705 - [c37]Jerry Li, Guanghao Ye:
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time. NeurIPS 2020 - [c36]Sitan Chen, Jerry Li, Ankur Moitra:
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. NeurIPS 2020 - [c35]Samuel B. Hopkins, Jerry Li, Fred Zhang:
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization. NeurIPS 2020 - [c34]Arun Jambulapati, Jerry Li, Kevin Tian:
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. NeurIPS 2020 - [c33]Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco:
Sample Efficient Toeplitz Covariance Estimation. SODA 2020: 378-397 - [c32]Sitan Chen, Jerry Li, Zhao Song:
Learning mixtures of linear regressions in subexponential time via Fourier moments. STOC 2020: 587-600 - [c31]Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian:
Positive semidefinite programming: mixed, parallel, and width-independent. STOC 2020: 789-802 - [c30]Sitan Chen, Jerry Li, Ankur Moitra:
Efficiently learning structured distributions from untrusted batches. STOC 2020: 960-973 - 2019
- [c29]Samuel B. Hopkins, Jerry Li:
How Hard is Robust Mean Estimation? COLT 2019: 1649-1682 - [c28]Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman:
Privately Learning High-Dimensional Distributions. COLT 2019: 1853-1902 - [c27]Jerry Li, Aleksandar Nikolov, Ilya P. Razenshteyn, Erik Waingarten:
On Mean Estimation for General Norms with Statistical Queries. COLT 2019: 2158-2172 - [c26]Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. ICML 2019: 1596-1606 - [c25]Yihe Dong, Samuel B. Hopkins, Jerry Li:
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. NeurIPS 2019: 6065-6075 - [c24]Hadi Salman, Jerry Li, Ilya P. Razenshteyn, Pengchuan Zhang, Huan Zhang, Sébastien Bubeck, Greg Yang:
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. NeurIPS 2019: 11289-11300 - 2018
- [c23]Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. COLT 2018: 819-842 - [c22]Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt:
On the Limitations of First-Order Approximation in GAN Dynamics. ICML 2018: 3011-3019 - [c21]Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li:
Byzantine Stochastic Gradient Descent. NeurIPS 2018: 4618-4628 - [c20]Brandon Tran, Jerry Li, Aleksander Madry:
Spectral Signatures in Backdoor Attacks. NeurIPS 2018: 8011-8021 - [c19]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. SODA 2018: 2683-2702 - [c18]Dan Alistarh, Trevor Brown, Justin Kopinsky, Jerry Zheng Li, Giorgi Nadiradze:
Distributionally Linearizable Data Structures. SPAA 2018: 133-142 - [c17]Samuel B. Hopkins, Jerry Li:
Mixture models, robustness, and sum of squares proofs. STOC 2018: 1021-1034 - 2017
- [c16]Sivaraman Balakrishnan, Simon S. Du, Jerry Li, Aarti Singh:
Computationally Efficient Robust Sparse Estimation in High Dimensions. COLT 2017: 169-212 - [c15]Jerry Li, Ludwig Schmidt:
Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities. COLT 2017: 1302-1382 - [c14]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Being Robust (in High Dimensions) Can Be Practical. ICML 2017: 999-1008 - [c13]Hantian Zhang, Jerry Li, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang:
ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning. ICML 2017: 4035-4043 - [c12]Dan Alistarh, Demjan Grubic, Jerry Li, Ryota Tomioka, Milan Vojnovic:
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding. NIPS 2017: 1709-1720 - [c11]Ilias Diakonikolas, Elena Grigorescu, Jerry Li, Abhiram Natarajan, Krzysztof Onak, Ludwig Schmidt:
Communication-Efficient Distributed Learning of Discrete Distributions. NIPS 2017: 6391-6401 - [c10]Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze:
The Power of Choice in Priority Scheduling. PODC 2017: 283-292 - [c9]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Sample-Optimal Density Estimation in Nearly-Linear Time. SODA 2017: 1278-1289 - 2016
- [c8]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High Dimensions without the Computational Intractability. FOCS 2016: 655-664 - [c7]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast Algorithms for Segmented Regression. ICML 2016: 2878-2886 - 2015
- [c6]Jerry Li, John Peebles:
Replacing Mark Bits with Randomness in Fibonacci Heaps. ICALP (1) 2015: 886-897 - [c5]Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Zheng Li, Ludwig Schmidt:
Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms. PODS 2015: 249-263 - [c4]Dan Alistarh, Justin Kopinsky, Jerry Li, Nir Shavit:
The SprayList: a scalable relaxed priority queue. PPoPP 2015: 11-20 - 2014
- [c3]Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu:
Counting of Query Expressions: Limitations of Propositional Methods. ICDT 2014: 177-188 - [c2]Rati Gelashvili, Mohsen Ghaffari, Jerry Li, Nir Shavit:
On the Importance of Registers for Computability. OPODIS 2014: 171-185 - 2013
- [c1]Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu:
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases. UAI 2013
Informal and Other Publications
- 2024
- [i70]Sitan Chen, Jerry Li, Allen Liu:
An optimal tradeoff between entanglement and copy complexity for state tomography. CoRR abs/2402.16353 (2024) - [i69]Arun Jambulapati, Syamantak Kumar, Jerry Li, Shourya Pandey, Ankit Pensia, Kevin Tian:
Black-Box k-to-1-PCA Reductions: Theory and Applications. CoRR abs/2403.03905 (2024) - 2023
- [i68]Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan:
Query lower bounds for log-concave sampling. CoRR abs/2304.02599 (2023) - [i67]Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, Michael Zeng:
Automatic Prompt Optimization with "Gradient Descent" and Beam Search. CoRR abs/2305.03495 (2023) - [i66]Clément L. Canonne, Samuel B. Hopkins, Jerry Li, Allen Liu, Shyam Narayanan:
The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination. CoRR abs/2307.10273 (2023) - [i65]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Matrix Completion in Almost-Verification Time. CoRR abs/2308.03661 (2023) - [i64]Arun Jambulapati, Jerry Li, Christopher Musco, Kirankumar Shiragur, Aaron Sidford, Kevin Tian:
Structured Semidefinite Programming for Recovering Structured Preconditioners. CoRR abs/2310.18265 (2023) - 2022
- [i63]Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka:
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. CoRR abs/2201.07206 (2022) - [i62]Jonathan A. Kelner, Jerry Li, Allen Liu, Aaron Sidford, Kevin Tian:
Semi-Random Sparse Recovery in Nearly-Linear Time. CoRR abs/2203.04002 (2022) - [i61]Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang:
Learning Polynomial Transformations. CoRR abs/2204.04209 (2022) - [i60]Sitan Chen, Brice Huang, Jerry Li, Allen Liu:
Tight Bounds for Quantum State Certification with Incoherent Measurements. CoRR abs/2204.07155 (2022) - [i59]Sitan Chen, Jerry Li, Yuanzhi Li:
Learning (Very) Simple Generative Models Is Hard. CoRR abs/2205.16003 (2022) - [i58]Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke:
Tight Bounds for State Tomography with Incoherent Measurements. CoRR abs/2206.05265 (2022) - [i57]Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang:
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. CoRR abs/2209.11215 (2022) - [i56]Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li:
The Complexity of NISQ. CoRR abs/2210.07234 (2022) - 2021
- [i55]Sitan Chen, Jerry Li, Ryan O'Donnell:
Toward Instance-Optimal State Certification With Incoherent Measurements. CoRR abs/2102.13098 (2021) - [i54]Jerry Li, Allen Liu, Ankur Moitra:
Sparsification for Sums of Exponentials and its Algorithmic Applications. CoRR abs/2106.02774 (2021) - [i53]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
Clustering Mixture Models in Almost-Linear Time via List-Decodable Mean Estimation. CoRR abs/2106.08537 (2021) - [i52]Arun Jambulapati, Jerry Li, Tselil Schramm, Kevin Tian:
Robust Regression Revisited: Acceleration and Improved Estimation Rates. CoRR abs/2106.11938 (2021) - [i51]Clément L. Canonne, Ayush Jain, Gautam Kamath, Jerry Li:
The Price of Tolerance in Distribution Testing. CoRR abs/2106.13414 (2021) - [i50]Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li:
A Hierarchy for Replica Quantum Advantage. CoRR abs/2111.05874 (2021) - [i49]Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li:
Exponential separations between learning with and without quantum memory. CoRR abs/2111.05881 (2021) - [i48]Jerry Li, Allen Liu:
Clustering Mixtures with Almost Optimal Separation in Polynomial Time. CoRR abs/2112.00706 (2021) - [i47]Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean:
Quantum advantage in learning from experiments. CoRR abs/2112.00778 (2021) - [i46]Sung Min Park, Kuo-An Wei, Kai Yuanqing Xiao, Jerry Li, Aleksander Madry:
On Distinctive Properties of Universal Perturbations. CoRR abs/2112.15329 (2021) - 2020
- [i45]Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian:
Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent. CoRR abs/2002.04830 (2020) - [i44]Greg Yang, Tony Duan, J. Edward Hu, Hadi Salman, Ilya P. Razenshteyn, Jerry Li:
Randomized Smoothing of All Shapes and Sizes. CoRR abs/2002.08118 (2020) - [i43]Sitan Chen, Jerry Li, Ankur Moitra:
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. CoRR abs/2002.10435 (2020) - [i42]Ilias Diakonikolas, Jerry Li, Anastasia Voloshinov:
Efficient Algorithms for Multidimensional Segmented Regression. CoRR abs/2003.11086 (2020) - [i41]Sébastien Bubeck, Sitan Chen, Jerry Li:
Entanglement is Necessary for Optimal Quantum Property Testing. CoRR abs/2004.07869 (2020) - [i40]Arun Jambulapati, Jerry Li, Kevin Tian:
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing. CoRR abs/2006.06980 (2020) - [i39]Jerry Li, Guanghao Ye:
Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time. CoRR abs/2006.13312 (2020) - [i38]Ivan Evtimov, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, Jerry Li:
Security and Machine Learning in the Real World. CoRR abs/2007.07205 (2020) - [i37]Samuel B. Hopkins, Jerry Li, Fred Zhang:
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization. CoRR abs/2007.15839 (2020) - [i36]Jerry Li, Aaron Sidford, Kevin Tian, Huishuai Zhang:
Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise. CoRR abs/2008.01722 (2020) - [i35]Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt:
Aligning AI With Shared Human Values. CoRR abs/2008.02275 (2020) - [i34]Matthew S. Brennan, Guy Bresler, Samuel B. Hopkins, Jerry Li, Tselil Schramm:
Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent. CoRR abs/2009.06107 (2020) - [i33]Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian:
List-Decodable Mean Estimation in Nearly-PCA Time. CoRR abs/2011.09973 (2020) - [i32]Zeyuan Allen-Zhu, Faeze Ebrahimian, Jerry Li, Dan Alistarh:
Byzantine-Resilient Non-Convex Stochastic Gradient Descent. CoRR abs/2012.14368 (2020) - 2019
- [i31]Jerry Li, Aleksandar Nikolov, Ilya P. Razenshteyn, Erik Waingarten:
On Mean Estimation for General Norms with Statistical Queries. CoRR abs/1902.02459 (2019) - [i30]Samuel B. Hopkins, Jerry Li:
How Hard Is Robust Mean Estimation? CoRR abs/1903.07870 (2019) - [i29]Yonina C. Eldar, Jerry Li, Cameron Musco, Christopher Musco:
Sample Efficient Toeplitz Covariance Estimation. CoRR abs/1905.05643 (2019) - [i28]Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya P. Razenshteyn, Sébastien Bubeck:
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. CoRR abs/1906.04584 (2019) - [i27]Yihe Dong, Samuel B. Hopkins, Jerry Li:
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection. CoRR abs/1906.11366 (2019) - [i26]Sitan Chen, Jerry Li, Ankur Moitra:
Efficiently Learning Structured Distributions from Untrusted Batches. CoRR abs/1911.02035 (2019) - [i25]Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco:
Low-Rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers. CoRR abs/1911.08015 (2019) - [i24]Sitan Chen, Jerry Li, Zhao Song:
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments. CoRR abs/1912.07629 (2019) - [i23]Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai:
Finding the Mode of a Kernel Density Estimate. CoRR abs/1912.07673 (2019) - 2018
- [i22]Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms. CoRR abs/1802.08513 (2018) - [i21]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart:
Sever: A Robust Meta-Algorithm for Stochastic Optimization. CoRR abs/1803.02815 (2018) - [i20]Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li:
Byzantine Stochastic Gradient Descent. CoRR abs/1803.08917 (2018) - [i19]Dan Alistarh, Trevor Brown, Justin Kopinsky, Jerry Zheng Li, Giorgi Nadiradze:
Distributionally Linearizable Data Structures. CoRR abs/1804.01018 (2018) - [i18]Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan R. Ullman:
Privately Learning High-Dimensional Distributions. CoRR abs/1805.00216 (2018) - [i17]Brandon Tran, Jerry Li, Aleksander Madry:
Spectral Signatures in Backdoor Attacks. CoRR abs/1811.00636 (2018) - 2017
- [i16]Jerry Li:
Robust Sparse Estimation Tasks in High Dimensions. CoRR abs/1702.05860 (2017) - [i15]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Being Robust (in High Dimensions) Can Be Practical. CoRR abs/1703.00893 (2017) - [i14]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart:
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently. CoRR abs/1704.03866 (2017) - [i13]Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze:
The Power of Choice in Priority Scheduling. CoRR abs/1706.04178 (2017) - [i12]Jerry Li, Aleksander Madry, John Peebles, Ludwig Schmidt:
Towards Understanding the Dynamics of Generative Adversarial Networks. CoRR abs/1706.09884 (2017) - [i11]Samuel B. Hopkins, Jerry Li:
Mixture Models, Robustness, and Sum of Squares Proofs. CoRR abs/1711.07454 (2017) - 2016
- [i10]Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Zheng Li, Ankur Moitra, Alistair Stewart:
Robust Estimators in High Dimensions without the Computational Intractability. CoRR abs/1604.06443 (2016) - [i9]Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt:
Fast Algorithms for Segmented Regression. CoRR abs/1607.03990 (2016) - [i8]Dan Alistarh, Jerry Li, Ryota Tomioka, Milan Vojnovic:
QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent. CoRR abs/1610.02132 (2016) - [i7]Hantian Zhang, Kaan Kara, Jerry Li, Dan Alistarh, Ji Liu, Ce Zhang:
ZipML: An End-to-end Bitwise Framework for Dense Generalized Linear Models. CoRR abs/1611.05402 (2016) - 2015
- [i6]Jayadev Acharya, Ilias Diakonikolas, Jerry Zheng Li, Ludwig Schmidt:
Sample-Optimal Density Estimation in Nearly-Linear Time. CoRR abs/1506.00671 (2015) - [i5]Jerry Zheng Li, Ludwig Schmidt:
A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k. CoRR abs/1506.01367 (2015) - 2014
- [i4]Jerry Li, John Peebles:
Replacing Mark Bits with Randomness in Fibonacci Heaps. CoRR abs/1407.2569 (2014) - [i3]Rati Gelashvili, Mohsen Ghaffari, Jerry Li, Nir Shavit:
On the Importance of Registers for Computability. CoRR abs/1411.0168 (2014) - 2013
- [i2]Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu:
Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases. CoRR abs/1309.6815 (2013) - [i1]Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu:
Model Counting of Query Expressions: Limitations of Propositional Methods. CoRR abs/1312.4125 (2013)
Coauthor Index
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last updated on 2024-04-14 01:15 CEST by the dblp team
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