Bayesian softmax-gated mixture-of-experts models achieve posterior contraction for density estimation and parameter recovery using Voronoi losses, plus two strategies for choosing the number of experts.
Mathematical Programming , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Introduces a robust satisficing model for screening under Wasserstein ambiguity that meets a revenue target by minimizing worst-case shortfall, yielding tractable randomized pricing mechanisms that enhance buyer surplus over robust optimization under increasing hazard rates.
DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
The authors create a distributionally robust formulation for the cyclic inventory routing problem that admits a deterministic reformulation via multi-point worst-case distributions and chance-constraint equivalents, solved by nested branch-and-price and tested on real automotive data.
citing papers explorer
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On Bayesian Softmax-Gated Mixture-of-Experts Models
Bayesian softmax-gated mixture-of-experts models achieve posterior contraction for density estimation and parameter recovery using Voronoi losses, plus two strategies for choosing the number of experts.
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From Optimization to Satisficing: Robust Screening under Distributional Ambiguity
Introduces a robust satisficing model for screening under Wasserstein ambiguity that meets a revenue target by minimizing worst-case shortfall, yielding tractable randomized pricing mechanisms that enhance buyer surplus over robust optimization under increasing hazard rates.
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Distributionally Robust Multi-Objective Optimization
DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
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The Distributionally Robust Cyclic Inventory Routing Problem
The authors create a distributionally robust formulation for the cyclic inventory routing problem that admits a deterministic reformulation via multi-point worst-case distributions and chance-constraint equivalents, solved by nested branch-and-price and tested on real automotive data.