5 papers
Conformalized Gaussian processes for online uncertainty quantification over graphs
Jinwen Xu, Qin Lu, Georgios B. Giannakis
Uncertainty quantification (UQ) over graphs arises in a number of safety-critical applications in network science. The Gaussian process (GP), as a classical Bayesian framework for…
Fine-tuning LLMs with variational Bayesian last layer for high-dimensional Bayesian optimization
Haotian Xiang, Jinwen Xu, Qin Lu
A plethora of applications entail solving black-box optimization problems with high evaluation costs, including drug discovery, material design, as well as hyperparameter tuning. T…
Adaptive Bayesian Optimization for Robust Identification of Stochastic Dynamical Systems
Jinwen Xu, Qin Lu, Yaakov Bar-Shalom
This paper deals with the identification of linear stochastic dynamical systems, where the unknowns include system coefficients and noise variances. Conventional approaches that re…
Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges
Haoran Lu, Luyang Fang, Ruidong Zhang +47
Due to the remarkable capabilities and growing impact of large language models (LLMs), they have been deeply integrated into many aspects of society. Thus, ensuring their alignment…
Online scalable Gaussian processes with conformal prediction for guaranteed coverage
Jinwen Xu, Qin Lu, Georgios B. Giannakis
The Gaussian process (GP) is a Bayesian nonparametric paradigm that is widely adopted for uncertainty quantification (UQ) in a number of safety-critical applications, including rob…