5 papers · 1 filter
FigBO: A Generalized Acquisition Function Framework with Look-Ahead Capability for Bayesian Optimization
Hui Chen, Xuhui Fan, Zhangkai Wu +1
Bayesian optimization is a powerful technique for optimizing expensive-to-evaluate black-box functions, consisting of two main components: a surrogate model and an acquisition func…
Federated Neural Nonparametric Point Processes
Hui Chen, Xuhui Fan, Hengyu Liu +5
Temporal point processes (TPPs) are effective for modeling event occurrences over time, but they struggle with sparse and uncertain events in federated systems, where privacy is a…
Marked Temporal Bayesian Flow Point Processes
Hui Chen, Xuhui Fan, Hengyu Liu +1
Marked event data captures events by recording their continuous-valued occurrence timestamps along with their corresponding discrete-valued types. They have appeared in various rea…
ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks
Zhangkai Wu, Xuhui Fan, Jin Li +3
The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discret…
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification
Hui Chen, Hengyu Liu, Zhangkai Wu +2
While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for f…