7 citations · 37 across the 12 of their papers we have counts for
12 papers
Multi-Objective Bayesian Optimization for Model Merging
Utkarsh Agarwal, Vamshi Bonagiri, Raul Astudillo +1
Model merging combines trained models directly in weight space, offering a compute-efficient alternative to additional fine-tuning. Selecting merge parameters is nevertheless diffi…
Bayesian preference elicitation for decision support in multiobjective optimization
Felix Huber, Sebastian Rojas Gonzalez, Raul Astudillo
We present a novel approach to help decision-makers efficiently identify preferred solutions from the Pareto set of a multi-objective optimization problem. Our method uses a Bayesi…
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Qian Xie, Raul Astudillo, Peter I. Frazier +2
Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite re…
Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong, Jiayue Wan, Raul Astudillo +3
Bayesian optimization is a powerful framework for optimizing functions that are expensive or time-consuming to evaluate. Recent work has considered Bayesian optimization of functio…
qEUBO: A Decision-Theoretic Acquisition Function for Preferential Bayesian Optimization
Raul Astudillo, Zhiyuan Jerry Lin, Eytan Bakshy +1
Preferential Bayesian optimization (PBO) is a framework for optimizing a decision maker's latent utility function using preference feedback. This work introduces the expected utili…
Preference Exploration for Efficient Bayesian Optimization with Multiple Outcomes
Zhiyuan Jerry Lin, Raul Astudillo, Peter I. Frazier +1
We consider Bayesian optimization of expensive-to-evaluate experiments that generate vector-valued outcomes over which a decision-maker (DM) has preferences. These preferences are…
Thinking inside the box: A tutorial on grey-box Bayesian optimization
Raul Astudillo, Peter I. Frazier
Bayesian optimization (BO) is a framework for global optimization of expensive-to-evaluate objective functions. Classical BO methods assume that the objective function is a black b…
Bayesian Optimization of Function Networks
Raul Astudillo, Peter I. Frazier
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 significan…
Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs
Raul Astudillo, Daniel R. Jiang, Maximilian Balandat +2
Bayesian optimization (BO) is a sample-efficient approach to optimizing costly-to-evaluate black-box functions. Most BO methods ignore how evaluation costs may vary over the optimi…
Bayesian Optimization of Risk Measures
Sait Cakmak, Raul Astudillo, Peter Frazier +1
We consider Bayesian optimization of objective functions of the form , where is a black-box expensive-to-evaluate function and denotes either the VaR or CVaR…
Multi-Attribute Bayesian Optimization With Interactive Preference Learning
Raul Astudillo, Peter I. Frazier
We consider black-box global optimization of time-consuming-to-evaluate functions on behalf of a decision-maker (DM) whose preferences must be learned. Each feasible design is asso…
Bayesian Optimization of Composite Functions
Raul Astudillo, Peter I. Frazier
We consider optimization of composite objective functions, i.e., of the form , where is a black-box derivative-free expensive-to-evaluate function with vector-val…