Microsoft Building 99
Redmond, WA 98052
jerrl AT microsoft DOT com
My CV (last updated 2/19/2018)I am a senior research scientist in the Machine Learning and Optimization Group at Microsoft Research Redmond.
In Fall 2018 I was the VMware Research Fellow at the Simons Institute. I did my Ph.D at MIT, where I was fortunate to work with Ankur Moitra. I also did my masters at MIT under the wonderful supervision of Nir Shavit.
My primary research interests are in learning theory and distributed algorithms, but I am broadly interested in many other things in TCS. I particularly like applications of analysis and analytic techniques to TCS problems.
As an undergrad at the University of Washington, I worked on complexity of branching programs, and how we could prove hardness of techniques used for naturally arising learning problems in database theory and AI.
In my free time I enjoy being remarkably mediocre at ultimate frisbee, chess, and piano, amongst other things.
I taught a course on robust machine learning at UW in Fall 2019! See the course website for more details. I am also making video lectures, covering and expanding upon some of the material covered in that course.
Principled Approaches to Robust Machine Learning and Beyond
Jerry Li.
Ph.D thesis
George M. Sprowls Award for outstanding Ph.D. theses in EECS at MIT
Note: any stupid jokes in the thesis are the author's own. Please excuse them.
The SprayList: A Scalable Relaxed Priority Queue
Jerry Li.
Master's thesis
Non-robust Features through the Lens of Universal Perturbations
Sung Min Park, Kuo-An Wei, Kai Xiao, Jerry Li, Aleksander Mądry
manuscript
List-Decodable Mean Estimation in Nearly-PCA Time
Ilias Diakonikolas, Daniel M. Kane, Daniel Kongsgaard, Jerry Li, Kevin Tian
manuscript
Statistical Query Algorithms and Low-Degree Tests Are Almost Equivalent
Matthew Brennan, Guy Bresler, Samuel B. Hopkins, Jerry Li, Tselil Schramm
manuscript
Security and Machine Learning in the Real World
Ivan Evtimov, Weidong Cui, Ece Kamar, Emre Kıcıman, Tadayoshi Kohno, Jerry Li
manuscript
Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semirandom Noise
Jerry Li, Aaron Sidford, Kevin Tian, Huishuai Zhang
manuscript
Finding the Mode of a Kernel Density Estimate
Jasper C.H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai
manuscript
Efficient Algorithms for Multidimensional Segmented Regression
Ilias Diakonikolas, Jerry Li, Anastasia Voloshinov
manuscript
Aligning AI With Shared Human Values
Dan Hendrycks, Collin Burns, Steven Basart, Andrew Critch, Jerry Li, Dawn Song, Jacob Steinhardt
to appear, ICLR 2021
Byzantine-Resilient Non-Convex Stochastic Gradient Descent
Dan Alistarh, Zeyuan Allen-Zhu, Faeze Ebrahimianghazani, Jerry Li
to appear, ICLR 2021
Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization
Samuel B. Hopkins, Jerry Li, Fred Zhang
NeurIPS 2020
Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing
Arun Jambulapati, Jerry Li, Kevin Tian
NeurIPS 2020, Spotlight Presentation
Learning Structured Distributions From Untrusted Batches: Faster and Simpler
Sitan Chen, Jerry Li, Ankur Moitra
NeurIPS 2020
Robust Covariance Estimation in Nearly-Matrix Multiplication Time
Jerry Li, Guanghao Ye
NeurIPS 2020
Entanglement is Necessary for Optimal Quantum Property Testing
Sébastien Bubeck, Sitan Chen, Jerry Li
FOCS 2020
Randomized Smoothing of All Shapes and Sizes
Greg Yang, Tony Duan, Edward Hu, Hadi Salman, Ilya Razenshteyn, Jerry Li
ICML 2020
Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent
Arun Jambulapati, Yin Tat Lee, Jerry Li, Swati Padmanabhan, Kevin Tian
STOC 2020
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments
Sitan Chen, Jerry Li, Zhao Song
STOC 2020
Efficiently Learning Structured Distributions from Untrusted Batches
Sitan Chen, Jerry Li, Ankur Moitra
STOC 2020
Low-rank Toeplitz Matrix Estimation via Random Ultra-Sparse Rulers
Hannah Lawrence, Jerry Li, Cameron Musco, Christopher Musco
ICASSP 2020
The Sample Complexity of Toeplitz Covariance Estimation
Yonina Eldar, Jerry Li, Cameron Musco, Christopher Musco
SODA 2020
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya Razenshteyn, Sébastien Bubeck
NeurIPS 2019, Spotlight Presentation
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Yihe Dong, Samuel B. Hopkins, Jerry Li
NeurIPS 2019, Spotlight Presentation
SEVER: A Robust Meta-Algorithm for Stochastic Optimization
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Jacob Steinhardt, Alistair Stewart
preliminary version in SecML 2018, Oral Presentation
ICML 2019
How Hard is Robust Mean Estimation?
Samuel B. Hopkins, Jerry Li
COLT 2019
On Mean Estimation For General Norms with Statistical Queries
Jerry Li, Aleksandar Nikolov, Ilya Razenshteyn, Erik Waingarten
COLT 2019
Privately Learning High-Dimensional Distributions
Gautam Kamath, Jerry Li, Vikrant Singhal, Jonathan Ullman
preliminary version in TPDP 2018
COLT 2019
Byzantine Stochastic Gradient Descent
Dan Alistarh, Zeyuan Allen-Zhu, Jerry Li
NeurIPS 2018
On the limitations of first order approximation in GAN dynamics
Jerry Li, Aleksander Mądry, John Peebles, Ludwig Schmidt
preliminary version in PADL 2017 as Towards Understanding the Dynamics of Generative Adversarial Networks
ICML 2018
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms
Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
COLT 2018
Distributionally Linearizable Data Structures
Dan Alistarh, Trevor Brown, Justin Kopinsky, Jerry Li, Giorgi Nadiradze
SPAA 2018
Mixture Models, Robustness, and Sum of Squares Proofs
Samuel B. Hopkins, Jerry Li
STOC 2018
Robustly Learning a Gaussian: Getting Optimal Error, Efficiently
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
SODA 2018
QSGD: Communication-Optimal Stochastic Gradient Descent, with Applications to Training Neural Networks
Dan Alistarh, Demjan Grubić, Jerry Li, Ryota Tomioka, Milan Vojnovic
preliminary version in OPT 2016
NIPS 2017, Spotlight Presentation
Invited for presentation at NVIDIA GTC
[code][poster][video]
Being Robust (in High Dimensions) can be Practical
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
ICML 2017
[code]
ZipML: An End-to-end Bitwise Framework for Dense Generalized Linear Models
Hantian Zhang*, Jerry Li*, Kaan Kara, Dan Alistarh, Ji Liu, Ce Zhang
*equal contribution
ICML 2017
The Power of Choice in Priority Scheduling
Dan Alistarh, Justin Kopinsky, Jerry Li, Giorgi Nadiradze
PODC 2017
Robust Sparse Estimation Tasks in High Dimensions
Jerry Li
COLT 2017
merged with this paper
Robust Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities
Jerry Li, Ludwig Schmidt.
COLT 2017
Sample Optimal Density Estimation in Nearly-Linear Time
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt.
SODA 2017
TCS+ talk by Ilias, which discussed the piecewise polynomial framework and our results at a high level
Robust Estimators in High Dimensions, without the
Computational Intractability
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Ankur Moitra, Alistair Stewart
FOCS 2016
Invited to Highlights of Algorithms 2017
Invited to appear in special issue of SIAM Journal on Computing for FOCS 2016.
Invited to appear in Communications of the ACM Research Highlights.
MIT News, USC Viterbi News
Fast Algorithms for Segmented Regression
Jayadev Acharya, Ilias Diakonikolas, Jerry Li, Ludwig Schmidt
ICML 2016
Replacing Mark Bits with Randomness in Fibonacci Heaps
Jerry Li, John Peebles.
ICALP 2015
Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms
Jayadev Acharya, Ilias Diakonikolas, Chinmay Hegde, Jerry Li, Ludwig Schmidt.
PODS 2015
The SprayList: A Scalable Relaxed Priority Queue
Dan Alistarh, Justin Kopinsky, Jerry Li, Nir Shavit.
PPoPP 2015, Best Artifact Award
See also the full version
[code]
Slashdot, MIT News
On the Importance of Registers for Computability
Rati Gelashvili, Mohsen Ghaffari, Jerry Li, Nir Shavit.
OPODIS 2014
Model Counting of Query Expressions: Limitations of Propositional Methods
Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu.
ICDT 2014
Invited to appear in special issue of ACM Transactions on Database Systems for ICDT 2014.
Lower bounds for exact model counting and applications in probabilistic databases
Paul Beame, Jerry Li, Sudeepa Roy, and Dan Suciu.
UAI 2013, selected for plenary presentation.
Robust Estimators in High Dimensions without the Computational Intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, Alistair Stewart.
SIAM Journal on Computing, 48(2), 2019. Special Issue for FOCS 2016.
Exact Model Counting of Query Expressions: Limitations of Propositional Methods
Paul Beame, Jerry Li, Sudeepa Roy, Dan Suciu.
ACM Transactions on Database Systems (TODS), Vol. 42, Issue 1, pages 1:1-1:46, March 2017.
Efficient training of neural networks
Dan Alistarh, Jerry Li, Ryota Tomioka, Milan Vojnovic
Tracking Serial Criminals with a Road Metric
Mark Bun, Jerry Li, Ian Zemke.
Our 2010 MCM submission, which was awarded an Outstanding Winner prize (the top prize).
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
Columbia Theory Seminar, July 2019
Efficiently Learning from Untrusted Batches
NYU Computation, Information, and Mathematics Seminar, July 2019
The Sample Complexity of Toeplitz Covariance Estimation
MIT Algorithms and Complexity Seminar, May 2019
Efficient Algorithms for High Dimensional Robust Learning
MSR AI Seminar, April 2019
Nearly Optimal Algorithms for Robust Mean Estimation
MIT Algorithms and Complexity Seminar, February 2019
TTIC Machine Learning Seminar, February 2019
MSR MLO Lunch, January 2019
UW Theory Seminar, January 2019
"Explicitly" Learning Mixtures of Gaussians
Simons Fellows Reading Group, September 2018
Robustly Learning a Gaussian in High Dimensions: Getting Optimal Error, Efficiently
SODA 2018, January 2018
Mixture Models, Robustness, and Sum-of-Squares Proofs
Google Algorithms Reading Group, July 2018
Microsoft Research Redmond, December 2017
MIT Algorithms and Complexity Semniar, November 2017
QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding
NIPS 2017, December 2017
Being Robust (in High Dimensions) can be Practical
ICML 2017, August 2017
Robust Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities
COLT 2017, July 2017
Efficient Robust Sparse Estimation in High Dimensions
COLT 2017, July 2017. Joint with Simon Du
Robust Estimators In High Dimensions without the Computational Intractability [slides]
Quantized Stochastic Gradient Descent
MIT ML Tea, October 2016
Fast Algorithms for Segmented Regression [slides]
ICML 2016 [video]
Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms [slides]
PODS 2015
Model Counting of Query Expressions: Limitations of Propositional Methods [slides]
ICDT 2015
MIT Theory Lunch, 2014
I'm participating in Algorithms Office Hours. If you're affiliated with MIT, and have algorithmic questions, please contact us!
I am on the steering committee for SLOGN*
I organized the Great Ideas in Theoretical Computer Science (aka theory lunch) in the 2013-2014 academic year.
I stole the boombox from the Glorious Office 3 times, then promptly lost it back each time.