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We obtain robust and computationally efficient estimators for learning several linear models that achieve statistically optimal convergence rate under minimal distributional assumptions.
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The ellipsoid method and its consequences in combinatorial optimization
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Robust estimators in high dimensions without the computational intractability
Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2016
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Agnostic estimation of mean and covariance
Kevin A Lai, Anup B Rao, and Santosh Vempala · 2016
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Polynomial-time tensor decompositions with sum-of-squares
Tengyu Ma, Jonathan Shi, and David Steurer · 2016
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Polynomial-time tensor decompositions with sum-of-squares
Tengyu Ma, Jonathan Shi, and David Steurer · 2016
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Sum-of-squares certificates for maxima of random tensors on the sphere
Vijay V. S. P. Bhattiprolu, Venkatesan Guruswami, and Euiwoong Lee · 2017
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Consistent robust regression
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Moses Charikar, Jacob Steinhardt, and Gregory Valiant · 2017
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Being robust (in high dimensions) can be practical
Ilias Diakonikolas, Gautam Kamath, Daniel M. Kane, Jerry Li, Ankur Moitra, and Alistair Stewart · 2017
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Statistical query lower bounds for robust estimation of high-dimensional gaussians and gaussian mixtures
Ilias Diakonikolas, Daniel M. Kane, and Alistair Stewart · 2017
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Better agnostic clustering via relaxed tensor norms
Pravesh K. Kothari and Jacob Steinhardt · 2017
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High-dimensional robust mean estimation in nearly-linear time
Yu Cheng, Ilias Diakonikolas, and Rong Ge · 2019
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Faster algorithms for high-dimensional robust covariance estimation
Yu Cheng, Ilias Diakonikolas, Rong Ge, and David P. Woodruff · 2019
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Recent advances in algorithmic high-dimensional robust statistics
Ilias Diakonikolas and Daniel M Kane · 2019
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Sever: A robust meta-algorithm for stochastic optimization
Ilias Diakonikolas, Gautam Kamath, Daniel Kane, Jerry Li, Jacob Steinhardt, and Alistair Stewart · 2019
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Efficient algorithms and lower bounds for robust linear regression
Ilias Diakonikolas, Weihao Kong, and Alistair Stewart · 2019
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Semialgebraic Proofs and Efficient Algorithm Design
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Pravesh K. Kothari and David Steurer · 2017
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Outlier-robust moment-estimation via sum-of-squares
Pravesh K Kothari and David Steurer · 2017
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Strongly refuting random csps below the spectral threshold
Prasad Raghavendra, Satish Rao, and Tselil Schramm · 2017
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Resilience: A criterion for learning in the presence of arbitrary outliers
Jacob Steinhardt, Moses Charikar, and Gregory Valiant · 2017
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Fast and robust tensor decomposition with applications to dictionary learning
Tselil Schramm and David Steurer · 2017
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Robustly learning a gaussian: Getting optimal error, efficiently
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Sever: A robust meta-algorithm for stochastic optimization
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A robust spectral algorithm for overcomplete tensor decomposition
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List-decodable linear regression
Sushrut Karmalkar, Adam Klivans, and Pravesh Kothari · 2019
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Adaptive hard thresholding for near-optimal consistent robust regression
Arun Sai Suggala, Kush Bhatia, Pradeep Ravikumar, and Prateek Jain · 2019
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Generalized resilience and robust statistics
Banghua Zhu, Jiantao Jiao, and Jacob Steinhardt · 2019
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List-decodable subspace recovery via sum-of-squares
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Outlier-robust clustering of non-spherical mixtures
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