A generalization of probabilistic reparameterization allows gradient-based acquisition optimization in fully mixed-variable Bayesian optimization with Gaussian process surrogates for non-equidistant discrete spaces.
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The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.
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Bayesian Optimization for Mixed-Variable Problems in the Natural Sciences
A generalization of probabilistic reparameterization allows gradient-based acquisition optimization in fully mixed-variable Bayesian optimization with Gaussian process surrogates for non-equidistant discrete spaces.
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Experiment-as-Code Labs: A Declarative Stack for AI-Driven Scientific Discovery
The paper introduces Experiment-as-Code Labs as a declarative stack synthesizing AI agents, systems orchestration, and physical lab control for AI-driven discovery.