5 papers · 1 filter
Robust Bayesian Decision Making under Adversarial Uncertainty
Haripriya Harikumar, Sammie Katt, Yasir Zubayr Barlas +1
Scientific experiments are often designed to maximize information gain, yet in many applications the primary objective is to support reliable downstream decision-making. Existing d…
Efficient Adaptive Data Acquisition via Pretrained Belief Representations
Daolang Huang, Zhuoyue Huang, Conor Hassan +3
Learning effective policies for adaptive data acquisition remains challenging: posterior-based methods rely on surrogate models and posterior approximations that can be misspecifie…
Multi-Objective Bayesian Optimization via Adaptive \varepsilon-Constraints Decomposition
Yaohong Yang, Sammie Katt, Samuel Kaski
Multi-objective Bayesian optimization (MOBO) provides a principled framework for optimizing multiple expensive black-box functions. However, existing MOBO methods often struggle wi…
In-Context Black-Box Optimization with Unreliable Feedback
Nicolas Samuel Blumer, Julien Martinelli, Samuel Kaski
Black-box optimization in science and engineering often comes with side information: experts, simulators, pretrained predictors, or heuristics can suggest which candidates look pro…
Active Preference Learning over Latent Preference Archetypes for Many-Objective Bayesian Optimization
Manisha Dubey, Sebastiaan De Peuter, Wanrong Wang +1
Preference-based many-objective Bayesian optimization typically assumes that all pairwise comparisons arise from a single latent utility function, despite real decision makers ofte…