RLNS regularizes LNS to perform block Gibbs sampling under entropy, interpolating between pseudolikelihood and exact MLE for differentiable combinatorial optimization.
The elements of differentiable program- ming
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A framework learns constitutive priors from noisy data to enable PDE-constrained inverse design of elastic networks using latent variables, homotopy continuation, Chamfer distance matching, and neural smoothness constraints.
A feedback optimization pipeline for tri-level mobility games outperforms Bayesian optimization and genetic algorithms on Zurich multimodal data while identifying incentives that boost multimodal use.
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Regularized Large Neighborhood Search
RLNS regularizes LNS to perform block Gibbs sampling under entropy, interpolating between pseudolikelihood and exact MLE for differentiable combinatorial optimization.
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Constitutive Priors for Inverse Design
A framework learns constitutive priors from noisy data to enable PDE-constrained inverse design of elastic networks using latent variables, homotopy continuation, Chamfer distance matching, and neural smoothness constraints.
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Hierarchical Strategic Decision-Making in Layered Mobility Systems
A feedback optimization pipeline for tri-level mobility games outperforms Bayesian optimization and genetic algorithms on Zurich multimodal data while identifying incentives that boost multimodal use.
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