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Found 4 papers, 0 papers with code
Constrained Bayesian Optimization with Adaptive Active Learning of Unknown Constraints
Optimizing objectives under constraints, where both the objectives and constraints are black box functions, is a common scenario in real-world applications such as scientific experimental design, design of medical therapies, and industrial process optimization.
Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation
Our approach is easy to tune, and is able to focus on local region of the optimization space that can be tackled by existing BO methods.
Learning Representation for Bayesian Optimization with Collision-free Regularization
We show that even with proper network design, such learned representation often leads to collision in the latent space: two points with significantly different observations collide in the learned latent space, leading to degraded optimization performance.
Learning Collision-free Latent Space for Bayesian Optimization
To address this issue, we propose Collision-Free Latent Space Optimization (CoFLO), which employs a novel regularizer to reduce the collision in the learned latent space and encourage the mapping from the latent space to objective value to be Lipschitz continuous.
