4 papers
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…
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…
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…