A new constrained optimal transport duality is established and used to prove equilibrium existence in large indivisible-goods markets while correcting a flaw in Azevedo et al. (2013).
3 ed., Springer, Berlin and Heidelberg
6 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 6representative citing papers
A new fully conjugate Bayesian variable selection method for GLMs with posterior consistency guarantees for inclusion indicators and coefficients, implemented via Gibbs sampling.
Formalizes concept learning in sparse autoencoders as set alignment between human-defined and model-induced concepts, distinguishing detection, separation, and approximation with geometric conditions for neuron representation.
A new cooperative localization algorithm based on overlapping covariance intersection is fully distributed, provably recursively consistent, and scalable to ultra large-scale multi-agent systems without performance loss from ignored cross-correlations.
The nonparametric Kiefer-Weiss problem is solved by deriving an optimal stopping policy based on a two-dimensional statistic (likelihood ratio plus expected remaining sample size) whose randomization rule maps the likelihood ratio to an integer sample size.
Wasserstein least squares extends Euclidean least squares to distribution-valued responses via convex analysis, yielding n^{-1/2} rates under template deformation and faster barycenter rates than prior work.
citing papers explorer
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The Nonparametric Kiefer-Weiss Problem
The nonparametric Kiefer-Weiss problem is solved by deriving an optimal stopping policy based on a two-dimensional statistic (likelihood ratio plus expected remaining sample size) whose randomization rule maps the likelihood ratio to an integer sample size.
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Wasserstein Least Squares: A Canonical Regression Method for Probability Distributions
Wasserstein least squares extends Euclidean least squares to distribution-valued responses via convex analysis, yielding n^{-1/2} rates under template deformation and faster barycenter rates than prior work.