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Found 4 papers, 3 papers with code
Joint Composite Latent Space Bayesian Optimization
To effectively tackle these challenges, we introduce Joint Composite Latent Space Bayesian Optimization (JoCo), a novel framework that jointly trains neural network encoders and probabilistic models to adaptively compress high-dimensional input and output spaces into manageable latent representations.
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision maker's latent utility function using preference feedback.
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
We consider Bayesian optimization of expensive-to-evaluate experiments that generate vector-valued outcomes over which a decision-maker (DM) has preferences.
Probability Paths and the Structure of Predictions over Time
Given a collection of such probability paths, we introduce a Bayesian framework -- which we call the Gaussian latent information martingale, or GLIM -- for modeling the structure of dynamic predictions over time.
