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=
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In high-frequency market making, action robustness (Sinkhorn regularization) dominates uncertainty tolerance in reshaping sequential quoting and inventory, while excessive robustness can cut execution opportunities in illiquid names.
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.
A large-deviations method generates plausible stress scenarios for financial losses by concentrating on most likely configurations conditional on large losses, recovering stressed loss laws even with sparse data.
Develops an exact finite-convergence algorithm for Σ₂^p-hard mixed-integer bilevel stochastic programs via extended single-level reformulation and stochastic cutting planes.
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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Robustness in Sequential Decision Making under Evolving Uncertainty: Evidence from High-Frequency Market Making
In high-frequency market making, action robustness (Sinkhorn regularization) dominates uncertainty tolerance in reshaping sequential quoting and inventory, while excessive robustness can cut execution opportunities in illiquid names.
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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.
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Generating Plausible Stress Scenarios via Large Deviations
A large-deviations method generates plausible stress scenarios for financial losses by concentrating on most likely configurations conditional on large losses, recovering stressed loss laws even with sparse data.
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An Exact Algorithm for Mixed-Integer Bilevel Stochastic Problem
Develops an exact finite-convergence algorithm for Σ₂^p-hard mixed-integer bilevel stochastic programs via extended single-level reformulation and stochastic cutting planes.