7 papers
When Do Multi-Agent Systems Help? An Information Bottleneck Perspective
Wendi Yu, Lianhao Zhou, Xiangjue Dong +6
LLM powered multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, their advantages over single-agent systems (SAS) remain unclear, with perform…
Goal-driven Bayesian Optimal Experimental Design for Robust Decision-Making Under Model Uncertainty
Jinwoo Go, Xiaoning Qian, Byung-Jun Yoon
Bayesian optimal experimental design (BOED) selects experiments to maximize information gain about model parameters. However, in decision-critical settings, reducing parameter unce…
C-LoRA: Contextual Low-Rank Adaptation for Uncertainty Estimation in Large Language Models
Amir Hossein Rahmati, Sanket Jantre, Weifeng Zhang +4
Low-Rank Adaptation (LoRA) offers a cost-effective solution for fine-tuning large language models (LLMs), but it often produces overconfident predictions in data-scarce few-shot se…
A Benchmark for Quantum Chemistry Relaxations via Machine Learning Interatomic Potentials
Cong Fu, Yuchao Lin, Zachary Krueger +8
Computational quantum chemistry plays a critical role in drug discovery, chemical synthesis, and materials science. While first-principles methods, such as density functional theor…
Cost-effective Reduced-Order Modeling via Bayesian Active Learning
Amir Hossein Rahmati, Nathan M. Urban, Byung-Jun Yoon +1
Machine Learning surrogates have been developed to accelerate solving systems dynamics of complex processes in different science and engineering applications. To faithfully capture…
Epidemiological Model Calibration via Graybox Bayesian Optimization
Puhua Niu, Byung-Jun Yoon, Xiaoning Qian
In this study, we focus on developing efficient calibration methods via Bayesian decision-making for the family of compartmental epidemiological models. The existing calibration me…