9 papers
Propose to Learn, Learn to Propose: Evaluability-Aware Assistance under Bounded Rationality
Yifan Zhu, Sammie Katt, Samuel Kaski
AI assistants often collaborate by proposing candidate edits, plans, or designs that users evaluate before adoption. Existing assistance methods focus on proposal quality or user-g…
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…
Generative-Model Predictive Planning for Navigation in Partially Observable Environments
Thomas Quilter, Yifan Zhu, Guorui Quan +2
Navigation in partially observable environments presents a significant challenge for autonomous agents, requiring effective decision-making with limited sensory information in unkn…
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…
Constrained Bayesian Experimental Design via Online Planning
Yujia Guo, Daolang Huang, Xinyu Zhang +3
Bayesian experimental design (BED) is a principled framework for data-efficient design of sequential experiments. However, existing BED methods are unable to adapt to dynamic const…
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…
Online Learning of Human Constraints from Feedback in Shared Autonomy
Shibei Zhu, Tran Nguyen Le, Samuel Kaski +1
Real-time collaboration with humans poses challenges due to the different behavior patterns of humans resulting from diverse physical constraints. Existing works typically focus on…