From the 1 of 7 linked papers with an AI index.
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Robust Bayesian Decision Making under Adversarial Uncertainty
Haripriya Harikumar, Sammie Katt, Yasir Zubayr Barlas +1
The paper proposes a Bayesian experimental design framework that accounts for worst‑case hidden effects, aiming to make downstream decisions stable and reliable even under adversar…
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…
Mixture-Model Preference Learning for Many-Objective Bayesian Optimization
Manisha Dubey, Sebastiaan De Peuter, Wanrong Wang +1
Preference-based many-objective optimization faces two obstacles: an expanding space of trade-offs and heterogeneous, context-dependent human value structures. Towards this, we pro…