Conditional diffusion models trained with BO-aware strategies approximate the optimum distribution, enabling a Diffusion-based Mode Seeking acquisition function with a sub-optimality guarantee that outperforms baselines in experiments.
Diff-bbo: Diffusion- based inverse modeling for black-box optimization
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SPARROW is a black-box optimization method that treats a fixed generative sampler as a structured proposal operator and applies rank-based selection over evaluated candidates to achieve low-budget optimization with asymptotic convergence guarantees over the sampler support.
GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.
citing papers explorer
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Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
Conditional diffusion models trained with BO-aware strategies approximate the optimum distribution, enabling a Diffusion-based Mode Seeking acquisition function with a sub-optimality guarantee that outperforms baselines in experiments.
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Generative Refinement for Low-Budget Black-Box Optimization
SPARROW is a black-box optimization method that treats a fixed generative sampler as a structured proposal operator and applies rank-based selection over evaluated candidates to achieve low-budget optimization with asymptotic convergence guarantees over the sampler support.
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GOAL: Graph-based Objective-Aligned Diffusion Solvers for Dynamic Multi-Objective Optimization
GOAL uses conditioned diffusion on relational graphs with typed edges to produce feasible multi-objective solutions for scheduling problems, reporting 100% feasibility and sub-0.2% MAPE on FSP, JSP, and FJSP up to 20 jobs.