
Robust Regression Revisited: Acceleration and Improved Estimation Rates
We study fast algorithms for statistical regression problems under the s...
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Clustering Mixture Models in AlmostLinear Time via ListDecodable Mean Estimation
We study the problem of listdecodable mean estimation, where an adversa...
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Sparsification for Sums of Exponentials and its Algorithmic Applications
Many works in signal processing and learning theory operate under the as...
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Toward InstanceOptimal State Certification With Incoherent Measurements
We revisit the basic problem of quantum state certification: given copie...
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ByzantineResilient NonConvex Stochastic Gradient Descent
We study adversaryresilient stochastic distributed optimization, in whi...
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ListDecodable Mean Estimation in NearlyPCA Time
Traditionally, robust statistics has focused on designing estimators tol...
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Statistical Query Algorithms and LowDegree Tests Are Almost Equivalent
Researchers currently use a number of approaches to predict and substant...
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Aligning AI With Shared Human Values
We show how to assess a language model's knowledge of basic concepts of ...
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WellConditioned Methods for IllConditioned Systems: Linear Regression with SemiRandom Noise
Classical iterative algorithms for linear system solving and regression ...
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Robust and HeavyTailed Mean Estimation Made Simple, via Regret Minimization
We study the problem of estimating the mean of a distribution in high di...
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Security and Machine Learning in the Real World
Machine learning (ML) models deployed in many safety and businesscriti...
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RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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Robust Gaussian Covariance Estimation in NearlyMatrix Multiplication Time
Robust covariance estimation is the following, wellstudied problem in h...
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Robust SubGaussian Principal Component Analysis and WidthIndependent Schatten Packing
We develop two methods for the following fundamental statistical task: g...
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Entanglement is Necessary for Optimal Quantum Property Testing
There has been a surge of progress in recent years in developing algorit...
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Efficient Algorithms for Multidimensional Segmented Regression
We study the fundamental problem of fixed design multidimensional segme...
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An empirical investigation of the challenges of realworld reinforcement learning
Reinforcement learning (RL) has proven its worth in a series of artifici...
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Learning Structured Distributions From Untrusted Batches: Faster and Simpler
We revisit the problem of learning from untrusted batches introduced by ...
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Randomized Smoothing of All Shapes and Sizes
Randomized smoothing is a recently proposed defense against adversarial ...
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Positive Semidefinite Programming: Mixed, Parallel, and WidthIndependent
We give the first approximation algorithm for mixed packing and covering...
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Finding the Mode of a Kernel Density Estimate
Given points p_1, ..., p_n in R^d, how do we find a point x which maximi...
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Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments
We consider the problem of learning a mixture of linear regressions (MLR...
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LowRank Toeplitz Matrix Estimation via Random UltraSparse Rulers
We study how to estimate a nearly lowrank Toeplitz covariance matrix T ...
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Efficiently Learning Structured Distributions from Untrusted Batches
We study the problem, introduced by Qiao and Valiant, of learning from u...
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Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection
We study two problems in highdimensional robust statistics: robust mean...
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Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Recent works have shown the effectiveness of randomized smoothing as a s...
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Sample Efficient Toeplitz Covariance Estimation
We study the query complexity of estimating the covariance matrix T of a...
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How Hard Is Robust Mean Estimation?
Robust mean estimation is the problem of estimating the mean μ∈R^d of a ...
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On Mean Estimation for General Norms with Statistical Queries
We study the problem of mean estimation for highdimensional distributio...
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Spectral Signatures in Backdoor Attacks
A recent line of work has uncovered a new form of data poisoning: socal...
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TwinGAN  Unpaired CrossDomain Image Translation with WeightSharing GANs
We present a framework for translating unlabeled images from one domain ...
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Privately Learning HighDimensional Distributions
We design nearly optimal differentially private algorithms for learning ...
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Byzantine Stochastic Gradient Descent
This paper studies the problem of distributed stochastic optimization in...
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Sever: A Robust MetaAlgorithm for Stochastic Optimization
In high dimensions, most machine learning methods are brittle to even a ...
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Fast and Sample NearOptimal Algorithms for Learning Multidimensional Histograms
We study the problem of robustly learning multidimensional histograms. ...
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Mixture Models, Robustness, and Sum of Squares Proofs
We use the Sum of Squares method to develop new efficient algorithms for...
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Robustly Learning a Gaussian: Getting Optimal Error, Efficiently
We study the fundamental problem of learning the parameters of a highdi...
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Being Robust (in High Dimensions) Can Be Practical
Robust estimation is much more challenging in high dimensions than it is...
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The ZipML Framework for Training Models with EndtoEnd Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
Recently there has been significant interest in training machinelearnin...
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Robust Estimators in High Dimensions without the Computational Intractability
We study highdimensional distribution learning in an agnostic setting w...
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Lower Bounds for Exact Model Counting and Applications in Probabilistic Databases
The best current methods for exactly computing the number of satisfying ...
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Jerry Li
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