Jerry Li

Affiliations:
  • Microsoft Research AI
  • Massachusetts Institute of Technology (MIT) (former)


According to our database1, Jerry Li authored at least 86 papers between 2013 and 2024.

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Bibliography

2024
Black-Box k-to-1-PCA Reductions: Theory and Applications.
CoRR, 2024

An optimal tradeoff between entanglement and copy complexity for state tomography.
CoRR, 2024

2023
Learning Polynomial Transformations via Generalized Tensor Decompositions.
Proceedings of the 55th Annual ACM Symposium on Theory of Computing, 2023

Structured Semidefinite Programming for Recovering Structured Preconditioners.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Matrix Completion in Almost-Verification Time.
Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

Query lower bounds for log-concave sampling.
Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

When Does Adaptivity Help for Quantum State Learning?
Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

The Full Landscape of Robust Mean Testing: Sharp Separations between Oblivious and Adaptive Contamination.
Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

Automatic Prompt Optimization with "Gradient Descent" and Beam Search.
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Semi-Random Sparse Recovery in Nearly-Linear Time.
Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022
The Complexity of NISQ.
CoRR, 2022

Tight Bounds for State Tomography with Incoherent Measurements.
CoRR, 2022

Learning Polynomial Transformations.
CoRR, 2022

Clustering mixtures with almost optimal separation in polynomial time.
Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

Clustering mixture models in almost-linear time via list-decodable mean estimation.
Proceedings of the STOC '22: 54th Annual ACM SIGACT Symposium on Theory of Computing, Rome, Italy, June 20, 2022

Robust Model Selection and Nearly-Proper Learning for GMMs.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Learning (Very) Simple Generative Models Is Hard.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs.
Proceedings of the Tenth International Conference on Learning Representations, 2022

Tight Bounds for Quantum State Certification with Incoherent Measurements.
Proceedings of the 63rd IEEE Annual Symposium on Foundations of Computer Science, 2022

Toward Instance-Optimal State Certification With Incoherent Measurements.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

The Price of Tolerance in Distribution Testing.
Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

2021
On Distinctive Properties of Universal Perturbations.
CoRR, 2021

Quantum advantage in learning from experiments.
CoRR, 2021

A Hierarchy for Replica Quantum Advantage.
CoRR, 2021

Sparsification for Sums of Exponentials and its Algorithmic Applications.
CoRR, 2021

Robustness meets algorithms.
Commun. ACM, 2021

Robust Regression Revisited: Acceleration and Improved Estimation Rates.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

List-Decodable Mean Estimation in Nearly-PCA Time.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Aligning AI With Shared Human Values.
Proceedings of the 9th International Conference on Learning Representations, 2021

Byzantine-Resilient Non-Convex Stochastic Gradient Descent.
Proceedings of the 9th International Conference on Learning Representations, 2021

Exponential Separations Between Learning With and Without Quantum Memory.
Proceedings of the 62nd IEEE Annual Symposium on Foundations of Computer Science, 2021

Finding an Approximate Mode of a Kernel Density Estimate.
Proceedings of the 29th Annual European Symposium on Algorithms, 2021

Statistical Query Algorithms and Low Degree Tests Are Almost Equivalent.
Proceedings of the Conference on Learning Theory, 2021

2020
Well-Conditioned Methods for Ill-Conditioned Systems: Linear Regression with Semi-Random Noise.
CoRR, 2020

Security and Machine Learning in the Real World.
CoRR, 2020

Efficient Algorithms for Multidimensional Segmented Regression.
CoRR, 2020

Positive semidefinite programming: mixed, parallel, and width-independent.
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020

Learning mixtures of linear regressions in subexponential time via Fourier moments.
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020

Efficiently learning structured distributions from untrusted batches.
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing, 2020

Sample Efficient Toeplitz Covariance Estimation.
Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms, 2020

Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning Structured Distributions From Untrusted Batches: Faster and Simpler.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Randomized Smoothing of All Shapes and Sizes.
Proceedings of the 37th International Conference on Machine Learning, 2020

Low-Rank Toeplitz Matrix Estimation Via Random Ultra-Sparse Rulers.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

Entanglement is Necessary for Optimal Quantum Property Testing.
Proceedings of the 61st IEEE Annual Symposium on Foundations of Computer Science, 2020

2019
Robust Estimators in High-Dimensions Without the Computational Intractability.
SIAM J. Comput., 2019

Finding the Mode of a Kernel Density Estimate.
CoRR, 2019

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Sever: A Robust Meta-Algorithm for Stochastic Optimization.
Proceedings of the 36th International Conference on Machine Learning, 2019

On Mean Estimation for General Norms with Statistical Queries.
Proceedings of the Conference on Learning Theory, 2019

Privately Learning High-Dimensional Distributions.
Proceedings of the Conference on Learning Theory, 2019

How Hard is Robust Mean Estimation?
Proceedings of the Conference on Learning Theory, 2019

2018
Principled approaches to robust machine learning and beyond.
PhD thesis, 2018

Mixture models, robustness, and sum of squares proofs.
Proceedings of the 50th Annual ACM SIGACT Symposium on Theory of Computing, 2018

Distributionally Linearizable Data Structures.
Proceedings of the 30th on Symposium on Parallelism in Algorithms and Architectures, 2018

Robustly Learning a Gaussian: Getting Optimal Error, Efficiently.
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms, 2018

Spectral Signatures in Backdoor Attacks.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Byzantine Stochastic Gradient Descent.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On the Limitations of First-Order Approximation in GAN Dynamics.
Proceedings of the 35th International Conference on Machine Learning, 2018

Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms.
Proceedings of the Conference On Learning Theory, 2018

2017
Exact Model Counting of Query Expressions: Limitations of Propositional Methods.
ACM Trans. Database Syst., 2017

Robust Sparse Estimation Tasks in High Dimensions.
CoRR, 2017

Towards Understanding the Dynamics of Generative Adversarial Networks.
CoRR, 2017

Sample-Optimal Density Estimation in Nearly-Linear Time.
Proceedings of the Twenty-Eighth Annual ACM-SIAM Symposium on Discrete Algorithms, 2017

The Power of Choice in Priority Scheduling.
Proceedings of the ACM Symposium on Principles of Distributed Computing, 2017

Communication-Efficient Distributed Learning of Discrete Distributions.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

ZipML: Training Linear Models with End-to-End Low Precision, and a Little Bit of Deep Learning.
Proceedings of the 34th International Conference on Machine Learning, 2017

Being Robust (in High Dimensions) Can Be Practical.
Proceedings of the 34th International Conference on Machine Learning, 2017

Computationally Efficient Robust Sparse Estimation in High Dimensions.
Proceedings of the 30th Conference on Learning Theory, 2017

Robust and Proper Learning for Mixtures of Gaussians via Systems of Polynomial Inequalities.
Proceedings of the 30th Conference on Learning Theory, 2017

2016
ZipML: An End-to-end Bitwise Framework for Dense Generalized Linear Models.
CoRR, 2016

QSGD: Randomized Quantization for Communication-Optimal Stochastic Gradient Descent.
CoRR, 2016

Fast Algorithms for Segmented Regression.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k.
CoRR, 2015

The SprayList: a scalable relaxed priority queue.
Proceedings of the 20th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, 2015

Fast and Near-Optimal Algorithms for Approximating Distributions by Histograms.
Proceedings of the 34th ACM Symposium on Principles of Database Systems, 2015

Replacing Mark Bits with Randomness in Fibonacci Heaps.
Proceedings of the Automata, Languages, and Programming - 42nd International Colloquium, 2015

2014
On the Importance of Registers for Computability.
Proceedings of the Principles of Distributed Systems - 18th International Conference, 2014

Counting of Query Expressions: Limitations of Propositional Methods.
Proceedings of the Proc. 17th International Conference on Database Theory (ICDT), 2014

2013
Model Counting of Query Expressions: Limitations of Propositional Methods.
CoRR, 2013

Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases.
Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence, 2013


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