Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.
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9 Pith papers cite this work, alongside 54 external citations. Polarity classification is still indexing.
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Provides theoretical characterizations of detection delay for univariate online robust mean change point detection under Huber contamination and heavy tails, plus a multivariate robust mean testing procedure, with matching lower bounds.
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
Defines threshold breakdown point and m-sensitivity for M-estimators, derives their properties, extends to hypothesis testing, and supplies consistency, asymptotic normality, and multiplier bootstrap results.
Realisable epsilon-contamination models for MNAR data yield minimax mean estimation rates that decompose into MCAR plus robust terms and remain consistent for Gaussian bases even as missingness and epsilon both tend to 1.
New smoothed random-perturbation estimators for the mean achieve exponential concentration bounds using only finite second-moment assumptions.
Derives VC-dimension-based error bounds for MOM mean estimators and introduces MOM halfspace depth estimator under finite second moment assumptions.
A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.
Develops truncated-gradient mirror descent algorithms for robust convex stochastic optimization and establishes sub-Gaussian confidence bounds under weak noise tail assumptions in convex and strongly convex cases.
citing papers explorer
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Fast Computation of Free-Support Wasserstein Medians
Direct fixed-weight solver for free-support Wasserstein medians relocates atoms using OT barycentric projections and inverse-distance weights, achieving monotone descent on smoothed objectives with fewer subproblems than nested Weiszfeld baselines.
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Online change point detection under heavy-tailedness and contamination
Provides theoretical characterizations of detection delay for univariate online robust mean change point detection under Huber contamination and heavy tails, plus a multivariate robust mean testing procedure, with matching lower bounds.
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Scale-Calibrated Median-of-Means for Robust Distributed Principal Component Analysis
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
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The Threshold Breakdown Point
Defines threshold breakdown point and m-sensitivity for M-estimators, derives their properties, extends to hypothesis testing, and supplies consistency, asymptotic normality, and multiplier bootstrap results.
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Estimation beyond Missing (Completely) at Random
Realisable epsilon-contamination models for MNAR data yield minimax mean estimation rates that decompose into MCAR plus robust terms and remain consistent for Gaussian bases even as missingness and epsilon both tend to 1.
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Distribution-robust mean estimation via smoothed random perturbations
New smoothed random-perturbation estimators for the mean achieve exponential concentration bounds using only finite second-moment assumptions.
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Error bounds of Median-of-means estimators with VC-dimension
Derives VC-dimension-based error bounds for MOM mean estimators and introduces MOM halfspace depth estimator under finite second moment assumptions.
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Density Evolution: A Multiscale View of Density Estimation
A review reframing density estimation as 'density evolution' across scales, linking kernel smoothing to heat flow, mixtures to compression, and topology to level sets, while stating three structural results on modes, Gaussian semigroups, and log-concavity.
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Algorithms of Robust Stochastic Optimization Based on Mirror Descent Method
Develops truncated-gradient mirror descent algorithms for robust convex stochastic optimization and establishes sub-Gaussian confidence bounds under weak noise tail assumptions in convex and strongly convex cases.