10 papers
Language-Induced Priors for Domain Adaptation
Qiyuan Chen, Jiayu Zhou, Raed Al Kontar
Domain adaptation faces a fundamental paradox in the cold-start regime. When target data is scarce, statistical methods fail to distinguish relevant source domains from irrelevant…
Collaborative Contextual Bayesian Optimization
Chih-Yu Chang, Qiyuan Chen, Tianhan Gao +5
Discovering optimal designs through sequential data collection is essential in many real-world applications. While Bayesian Optimization (BO) has achieved remarkable success in thi…
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language Models
Haoyi Song, Ruihan Ji, Naichen Shi +2
Large language models (LLMs) have transformed natural language processing, but their reliable deployment requires effective uncertainty quantification (UQ). Existing UQ methods are…
LLINBO: Trustworthy LLM-in-the-Loop Bayesian Optimization
Chih-Yu Chang, Milad Azvar, Chinedum Okwudire +1
Bayesian optimization (BO) is a sequential decision-making tool widely used for optimizing expensive black-box functions. Recently, Large Language Models (LLMs) have shown remarkab…
Online Learning of Optimal Sequential Testing Policies
Qiyuan Chen, Raed Al Kontar
This paper studies an online learning problem that seeks optimal testing policies for a stream of subjects, each of whom can be evaluated through a sequence of candidate tests draw…
ALBATROSS: Cheap Filtration Based Geometry via Stochastic Sub-Sampling
Andrew J. Stier, Naichen Shi, Raed Al Kontar +2
Topological data analysis (TDA) detects geometric structure in biological data. However, many TDA algorithms are memory intensive and impractical for massive datasets. Here, we int…