A KL-divergence trust-region formulation for sampling-based MPC replaces heuristic hyperparameter adaptation with Lagrangian-optimal updates and improves convergence when combined with deterministic LCD sampling.
A randomized Halton algorithm in R
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Labrador is a domain-optimized neural posterior estimation tool achieving 1% median importance-sampling efficiency and first extensive coverage of long-duration low-mass gravitational wave signals through equivariance and a stable procedure for differing priors.
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Sampling-based Model Predictive Control Using Trust Regions
A KL-divergence trust-region formulation for sampling-based MPC replaces heuristic hyperparameter adaptation with Lagrangian-optimal updates and improves convergence when combined with deterministic LCD sampling.
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labrador: A domain-optimized machine-learning tool for gravitational wave inference
Labrador is a domain-optimized neural posterior estimation tool achieving 1% median importance-sampling efficiency and first extensive coverage of long-duration low-mass gravitational wave signals through equivariance and a stable procedure for differing priors.