2 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Online Bayesian Node Classification on Inductive Graphs under Distribution Shift
Jinwen Xu, Gonzalo Mateos Buckstein, Qin Lu
On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and calibrated uncertainty for saf…
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…
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…