1 citations · 1 across the 4 of their papers we have counts for
8 papers
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…
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…
A Collaborative Process Parameter Recommender System for Fleets of Networked Manufacturing Machines -- with Application to 3D Printing
Weishi Wang, Sicong Guo, Chenhuan Jiang +5
Fleets of networked manufacturing machines of the same type, that are collocated or geographically distributed, are growing in popularity. An excellent example is the rise of 3D pr…
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…