137 papers with code • 0 benchmarks • 0 datasets
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Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.
We offer an experimental benchmark and empirical study for off-policy policy evaluation (OPE) in reinforcement learning, which is a key problem in many safety critical applications.
Model-based methods founded on quantitative descriptions of gene regulation are among the most promising, but many such methods rely on simple, local models or on ad hoc inference approaches lacking experimental interpretability.
Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning).
This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios.
Bayesian optimization provides sample-efficient global optimization for a broad range of applications, including automatic machine learning, engineering, physics, and experimental design.