Byzantine-robust distributed sparse learning for \(M\)-estimation

DOI10.1007/s10994-021-06001-xOpenAlexW3184946322MaRDI QIDQ6053803

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Publication date: 24 October 2023

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-021-06001-x

zbMATH Keywords

\(M\)-estimationsupport recoverymedian-of-meansByzantine robustness

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Distributed systems (68M14) Distributed algorithms (68W15)

Related Items (2)

Discussion of: ‘A review of distributed statistical inference’ ⋮ High-dimensional \(M\)-estimation for Byzantine-robust decentralized learning

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Geometric median and robust estimation in Banach spaces
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Statistics for high-dimensional data. Methods, theory and applications.
• Support recovery without incoherence: a case for nonconvex regularization
• Robust regression: Asymptotics, conjectures and Monte Carlo
• The landscape of empirical risk for nonconvex losses
• Implicit regularization in nonconvex statistical estimation: gradient descent converges linearly for phase retrieval, matrix completion, and blind deconvolution
• Robust machine learning by median-of-means: theory and practice
• Distributed statistical estimation and rates of convergence in normal approximation
• Regularization, sparse recovery, and median-of-means tournaments
• Adaptive robust variable selection
• On the ``degrees of freedom of the lasso
• The Byzantine Generals Problem
• Sharp Thresholds for High-Dimensional and Noisy Sparsity Recovery Using $\ell _{1}$-Constrained Quadratic Programming (Lasso)
• Communication-Efficient Distributed Statistical Inference
• Brief Announcement
• Tuning parameter selectors for the smoothly clipped absolute deviation method
• Regularized M-estimators with nonconvexity: Statistical and algorithmic theory for local optima
• Communication-Efficient Accurate Statistical Estimation

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