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Found 9 papers, 5 papers with code
Bayesian Optimization of Function Networks with Partial Evaluations
Recent work has considered Bayesian optimization of function networks (BOFN), where the objective function is computed via a network of functions, each taking as input the output of previous nodes in the network and additional parameters.
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.
Thinking inside the box: A tutorial on grey-box Bayesian optimization
However, internal information about objective function computation is often available.
Bayesian Optimization of Function Networks
We consider Bayesian optimization of the output of a network of functions, where each function takes as input the output of its parent nodes, and where the network takes significant time to evaluate.
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
To overcome the shortcomings of existing approaches, we propose the budgeted multi-step expected improvement, a non-myopic acquisition function that generalizes classical expected improvement to the setting of heterogeneous and unknown evaluation costs.
Bayesian Optimization of Risk Measures
We consider Bayesian optimization of objective functions of the form $\rho[ F(x, W) ]$, where $F$ is a black-box expensive-to-evaluate function and $\rho$ denotes either the VaR or CVaR risk measure, computed with respect to the randomness induced by the environmental random variable $W$.
Multi-Attribute Bayesian Optimization With Interactive Preference Learning
The outcome of our approach is a menu of designs and evaluated attributes from which the DM makes a final selection.
Bayesian Optimization of Composite Functions
We consider optimization of composite objective functions, i. e., of the form $f(x)=g(h(x))$, where $h$ is a black-box derivative-free expensive-to-evaluate function with vector-valued outputs, and $g$ is a cheap-to-evaluate real-valued function.
