A Refined MISD Algorithm Based on Gaussian Process Regression. - Feng Zhou 011; Zhidong Li; Xuhui Fan 001; Yang Wang 002; Arcot Sowmya; Fang Chen 001 (PAKDD (2), 2018)

Hawkes Process with Stochastic Triggering Kernel. - Feng Zhou 011; Yixuan Zhang 006; Zhidong Li; Xuhui Fan 001; Yang Wang 002; Arcot Sowmya; Fang Chen 001 (PAKDD (1), 2019)

Efficient EM-Variational Inference for Hawkes Process. - Feng Zhou 011 (CoRR, 2019)

Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation. - Feng Zhou 011; Zhidong Li; Xuhui Fan 001; Yang Wang 002; Arcot Sowmya; Fang Chen 001 (CoRR, 2019)

Additive Poisson Process: Learning Intensity of Higher-Order Interaction in Stochastic Processes. - Simon Luo; Feng Zhou 011; Lamiae Azizi; Mahito Sugiyama (CoRR, 2020)

Efficient Inference of Nonparametric Interaction in Spiking-neuron Networks. - Feng Zhou 011; Yixuan Zhang 006; Jun Zhu 001 (CoRR, 2020)

Fast multi-resolution segmentation for nonstationary Hawkes process using cumulants. - Feng Zhou 011; Zhidong Li; Xuhui Fan 001; Yang Wang 002; Arcot Sowmya; Fang Chen 001 (Int. J. Data Sci. Anal., 2020)

Bias-tolerant Fair Classification. - Yixuan Zhang 006; Feng Zhou 011; Zhidong Li; Yang Wang 002; Fang Chen 001 (ACML, 2021)

Efficient Inference of Flexible Interaction in Spiking-neuron Networks. - Feng Zhou 011; Yixuan Zhang 006; Jun Zhu 001 (ICLR, 2021)

Continuous-time edge modelling using non-parametric point processes. - Xuhui Fan 001; Bin Li 015; Feng Zhou 011; Scott A. Sisson (NeurIPS, 2021)

Nonlinear Hawkes Processes in Time-Varying System. - Feng Zhou 011; Quyu Kong; Yixuan Zhang 006; Cheng Feng 004; Jun Zhu 001 (CoRR, 2021)

Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer. - Shiye Lei; Zhuozhuo Tu; Leszek Rutkowski; Feng Zhou 011; Li Shen 008; Fengxiang He; Dacheng Tao (CoRR, 2021)

Efficient EM-variational inference for nonparametric Hawkes process. - Feng Zhou 011; Simon Luo; Zhidong Li; Xuhui Fan 001; Yang Wang 002; Arcot Sowmya; Fang Chen 001 (Stat. Comput., 2021)

Accelerated Linearized Laplace Approximation for Bayesian Deep Learning. - Zhijie Deng; Feng Zhou 011; Jun Zhu 001 (NeurIPS, 2022)

Deep Ensemble as a Gaussian Process Approximate Posterior. - Zhijie Deng; Feng Zhou 011; Jianfei Chen 001; Guoqiang Wu; Jun Zhu 001 (CoRR, 2022)

De-biased Representation Learning for Fairness with Unreliable Labels. - Yixuan Zhang 006; Feng Zhou 011; Zhidong Li; Yang Wang 002; Fang Chen 001 (CoRR, 2022)

Efficient Inference for Dynamic Flexible Interactions of Neural Populations. - Feng Zhou 011; Quyu Kong; Zhijie Deng; Jichao Kan; Yixuan Zhang 006; Cheng Feng 004; Jun Zhu 001 (J. Mach. Learn. Res., 2022)

Fair Representation Learning with Unreliable Labels. - Yixuan Zhang 006; Feng Zhou 011; Zhidong Li; Yang Wang 002; Fang Chen 001 (AISTATS, 2023)

Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes. - Yixuan Zhang 006; Quyu Kong; Feng Zhou 011 (NeurIPS, 2023)

pFedV: Mitigating Feature Distribution Skewness via Personalized Federated Learning with Variational Distribution Constraints. - Yongli Mou; Jiahui Geng; Feng Zhou 011; Oya Beyan; Chunming Rong; Stefan Decker (PAKDD (2), 2023)

Zero-shot Inversion Process for Image Attribute Editing with Diffusion Models. - Zhanbo Feng; Zenan Ling; Ci Gong; Feng Zhou 011; Jie Li 002; Robert C. Qiu (CoRR, 2023)

Heterogeneous multi-task Gaussian Cox processes. - Feng Zhou 011; Quyu Kong; Zhijie Deng; Fengxiang He; Peng Cui 007; Jun Zhu 001 (Mach. Learn., 2023)

Mitigating Label Bias in Machine Learning: Fairness through Confident Learning. - Yixuan Zhang 006; Boyu Li 003; Zenan Ling; Feng Zhou 011 (AAAI, 2024)

Expert-Guided Model Cultivation: CoTeaching to Resolve Abstruseness and Enhance Learning Performance. - Siqi Zhang; Feng Zhou 011; Zhidong Li; Yang Wang 002; Donglian Qi; Shuming Li (ADMA (2), 2024)

TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes. - Zizhuo Meng; Boyu Li 003; Xuhui Fan 001; Zhidong Li; Yang Wang 002; Fang Chen 001; Feng Zhou 011 (ECAI, 2024)

Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures. - Zenan Ling; Longbo Li; Zhanbo Feng; Yixuan Zhang 006; Feng Zhou 011; Robert C. Qiu; Zhenyu Liao 001 (ICML, 2024)

Interpretable Transformer Hawkes Processes: Unveiling Complex Interactions in Social Networks. - Zizhuo Meng; Ke Wan 002; Yadong Huang; Zhidong Li; Yang Wang 002; Feng Zhou 011 (KDD, 2024)

Nonstationary Sparse Spectral Permanental Process. - Zicheng Sun; Yixuan Zhang 006; Zenan Ling; Xuhui Fan 001; Feng Zhou 011 (NeurIPS, 2024)

Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency. - Yixuan Zhang 006; Feng Zhou 011 (CoRR, 2024)

Federated Neural Nonparametric Point Processes. - Hui Chen 026; Hengyu Liu 001; Yaqiong Li; Xuhui Fan 001; Zhilin Zhao 001; Feng Zhou 011; Christopher John Quinn; Longbing Cao (CoRR, 2024)

IGNN-Solver: A Graph Neural Solver for Implicit Graph Neural Networks. - Junchao Lin; Zenan Ling; Zhanbo Feng; Feng Zhou 011; Jingwen Xu; Robert C. Qiu (CoRR, 2024)

Series-to-Series Diffusion Bridge Model. - Hao Yang; Zhanbo Feng; Feng Zhou 011; Robert C. Qiu; Zenan Ling (CoRR, 2024)

Calibrating Deep Ensemble through Functional Variational Inference. - Zhijie Deng; Feng Zhou 011; Jianfei Chen 001; Guoqiang Wu; Jun Zhu 001 (Trans. Mach. Learn. Res., 2024)

Navigating Towards Fairness with Data Selection. - Yixuan Zhang 006; Zhidong Li; Yang Wang 002; Fang Chen 001; Xuhui Fan 001; Feng Zhou 011 (AAAI, 2025)

Textual and Visual Prompt Fusion for Image Editing via Step-Wise Alignment. - Zhanbo Feng; Zenan Ling; Xinyu Lu; Ci Gong; Feng Zhou 011; Wugedele Bao; Jie Li 002; Fan Yang 087; Robert C. Qiu (ICASSP, 2025)

Task Diversity in Bayesian Federated Learning: Simultaneous Processing of Classification and Regression. - Junliang Lyu; Yixuan Zhang 006; Xiaoling Lu; Feng Zhou 011 (KDD (1), 2025)

Advances in Temporal Point Processes: Bayesian, Deep, and LLM Approaches. - Feng Zhou 011; Quyu Kong; Yixuan Zhang 006 (CoRR, 2025)