6 papers
Scalable Bayesian Optimization of Composite Functions for Image-Based Inverse Problems in Materials Characterization
Dasol Yoon, Poompol Buathong, Chia-Hao Lee +3
Estimating physical parameters from scientific images is a common inverse problem in materials characterization that often relies on expensive physics-based simulations. In electro…
LLM-Derived Preference Judgments Are Not Self-Consistent
Matthew T. Ford, Francis Bahk, Jingjing Wang +4
Agents increasingly interpret a person's natural-language preferences by querying an LLM for numerical preference judgments, e.g., by asking how much the person would be willing to…
Better Protein Function Prediction by Modeling Survivorship Bias
Zhongmou Chao, Poompol Buathong, Ekaterina Selivanovitch +2
Protein sequence data from nature exhibits survivorship bias: we only observe data from those organisms that survive and reproduce, while non-functional protein mutations are elimi…
LISTEN to Your Preferences: An LLM Framework for Multi-Objective Selection
Adam S. Jovine, Tinghan Ye, Francis Bahk +4
Human experts often struggle to select the best option from a large set of items with multiple competing objectives, a process bottlenecked by the difficulty of formalizing complex…
Cost-aware Stopping for Bayesian Optimization
Qian Xie, Linda Cai, Alexander Terenin +2
In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive black-box functions in a cost-aware…
Fast Bayesian Optimization of Function Networks with Partial Evaluations
Poompol Buathong, Peter I. Frazier
Bayesian optimization of function networks (BOFN) is a framework for optimizing expensive-to-evaluate objective functions structured as networks, where some nodes' outputs serve as…