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
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative 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.
citing papers explorer
-
Constrained optimal transport with an application to large markets with indivisible goods
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).
-
Bayesian Variable Selection in Generalized Linear Models
A new fully conjugate Bayesian variable selection method for GLMs with posterior consistency guarantees for inclusion indicators and coefficients, implemented via Gibbs sampling.
-
A Geometric View for Understanding Concept Learning and Neuron Interpretation in Sparse Autoencoders
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.
-
Consistent Distributed Cooperative Localization for Ultra Large-Scale Multi-agent Systems
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.