25 citations · 49 across the 6 of their papers we have counts for
7 papers
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…
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…
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
Zhangkai Wu, Longbing Cao, Qi Zhang +2
Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detectio…
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations
Hui Chen, Hengyu Liu, Longbing Cao +1
Bayesian personalized federated learning (BPFL) addresses challenges in existing personalized FL (PFL). BPFL aims to quantify the uncertainty and heterogeneity within and across cl…
CVAE: Gaussian Copula-based VAE Differing Disentangled from Coupled Representations with Contrastive Posterior
Zhangkai Wu, Longbing Cao
We present a self-supervised variational autoencoder (VAE) to jointly learn disentangled and dependent hidden factors and then enhance disentangled representation learning by a sel…