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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8 Pith papers cite this work, alongside 2,811 external citations. Polarity classification is still indexing.
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Pith papers citing it
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2026 8representative citing papers
Proposes a scale-calibrated median-of-means estimator for robust aggregation of distributed PCA estimates on the product of Euclidean space and Grassmann manifold.
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
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.
AI will dissolve the keyboard's dominance in knowledge work through migration of its function into AI systems, turning workers into adversarial auditors of generated content.
The paper proposes a five-theorem framework predicting a threshold τ* where firms switch from time-based to output-based talent accounting, reads Korea's rising SG&A/revenue trend as pre-threshold overhead pressure, and forecasts a 1.5-2.0 pp TFP advantage for output-based firms by 2032.
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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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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Intrinsic effective sample size for manifold-valued Markov chain Monte Carlo via kernel discrepancy
An intrinsic effective sample size for manifold MCMC is defined via kernel discrepancy as the number of independent draws yielding equivalent expected squared discrepancy to the target.
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Profile Likelihood Inference for Anisotropic Hyperbolic Wrapped Normal Models on Hyperbolic Space
The profile maximum likelihood estimator for the location in anisotropic hyperbolic wrapped normal models is strongly consistent, asymptotically normal, and attains the Hájek-Le Cam minimax lower bound under squared geodesic loss.
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Scale selection for geometric medians on product manifolds
Joint location-scale minimization for geometric medians on product manifolds degenerates to marginal medians, and three new scale-selection methods restore identifiability with asymptotic guarantees.
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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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The Instrumental Dissolution of Typing: Why AI Challenges the Keyboard Era in Knowledge Work
AI will dissolve the keyboard's dominance in knowledge work through migration of its function into AI systems, turning workers into adversarial auditors of generated content.
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What Capital After Labor? Forecasting the Talent ROI Transition in the Human-AI Era
The paper proposes a five-theorem framework predicting a threshold τ* where firms switch from time-based to output-based talent accounting, reads Korea's rising SG&A/revenue trend as pre-threshold overhead pressure, and forecasts a 1.5-2.0 pp TFP advantage for output-based firms by 2032.