Xuhui Fan

CoRR, 2024

ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks.

[BibT_eX]

[DOI]

CoRR, 2024

FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification.

[BibT_eX]

[DOI]

CoRR, 2024

Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation.

[BibT_eX]

[DOI]

Yididiya Y. Nadew

Christopher John Quinn

Proceedings of the Forty-first International Conference on Machine Learning, 2024

TransFeat-TPP: An Interpretable Deep Covariate Temporal Point Processes.

[BibT_eX]

[DOI]

Proceedings of the ECAI 2024 - 27th European Conference on Artificial Intelligence, 19-24 October 2024, Santiago de Compostela, Spain, 2024

2023

Dynamic customer segmentation via hierarchical fragmentation-coagulation processes.

[BibT_eX]

[DOI]

Mach. Learn., January, 2023

Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2023

Bayesian Federated Learning: A Survey.

[BibT_eX]

[DOI]

CoRR, 2023

Free-Form Variational Inference for Gaussian Process State-Space Models.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Supervised Categorical Metric Learning With Schatten p-Norms.

[BibT_eX]

[DOI]

Yaqiong Li

Éric Gaussier

IEEE Trans. Cybern., 2022

Smoothing graphons for modelling exchangeable relational data.

[BibT_eX]

[DOI]

Mach. Learn., 2022

2021

Kernelized Sparse Bayesian Matrix Factorization.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2021

Efficient EM-variational inference for nonparametric Hawkes process.

[BibT_eX]

[DOI]

Stat. Comput., 2021

Decoupling Sparsity and Smoothness in Dirichlet Belief Networks.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Continuous-time edge modelling using non-parametric point processes.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bayesian Nonparametric Space Partitions: A Survey.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Poisson-Randomised DirBN: Large Mutation is Needed in Dirichlet Belief Networks.

[BibT_eX]

[DOI]

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures.

[BibT_eX]

[DOI]

IEEE Trans. Signal Process., 2020

Fast multi-resolution segmentation for nonstationary Hawkes process using cumulants.

[BibT_eX]

[DOI]

Int. J. Data Sci. Anal., 2020

Supervised Categorical Metric Learning with Schatten p-Norms.

[BibT_eX]

[DOI]

Éric Gaussier

CoRR, 2020

Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling.

[BibT_eX]

[DOI]

CoRR, 2020

Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Online Binary Space Partitioning Forests.

[BibT_eX]

[DOI]

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Fragmentation Coagulation Based Mixed Membership Stochastic Blockmodel.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019

Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein's Unbiased Risk Estimator.

[BibT_eX]

[DOI]

IEEE Trans. Image Process., 2019

Scalable Deep Generative Relational Models with High-Order Node Dependence.

[BibT_eX]

[DOI]

CoRR, 2019

Scalable Inference for Nonparametric Hawkes Process Using Pólya-Gamma Augmentation.

[BibT_eX]

[DOI]

CoRR, 2019

Binary Space Partitioning Forests.

[BibT_eX]

[DOI]

Scott Anthony Sisson

CoRR, 2019

Hawkes Process with Stochastic Triggering Kernel.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2019

Scalable Deep Generative Relational Model with High-Order Node Dependence.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Binary Space Partitioning Forest.

[BibT_eX]

[DOI]

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

A Refined MISD Algorithm Based on Gaussian Process Regression.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Corrosion Prediction on Sewer Networks with Sparse Monitoring Sites: A Case Study.

[BibT_eX]

[DOI]

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Rectangular Bounding Process.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

The Binary Space Partitioning-Tree Process.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Learning Nonparametric Relational Models by Conjugately Incorporating Node Information in a Network.

[BibT_eX]

[DOI]

IEEE Trans. Cybern., 2017

2016

Stochastic Patching Process.

[BibT_eX]

[DOI]

CoRR, 2016

Bayesian Optimization of Partition Layouts for Mondrian Processes.

[BibT_eX]

[DOI]

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Copula Mixed-Membership Stochastic Blockmodel.

[BibT_eX]

[DOI]

Richard Yi Da Xu

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

The Ostomachion Process.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Nonparametric Bayesian models for learning network coupling relationships

[BibT_eX]

[DOI]

PhD thesis, 2015

Dynamic Infinite Mixed-Membership Stochastic Blockmodel.

[BibT_eX]

[DOI]

Richard Yi Da Xu

IEEE Trans. Neural Networks Learn. Syst., 2015

A convergence theorem for graph shift-type algorithms.

[BibT_eX]

[DOI]

Pattern Recognit., 2015

2013

Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models

[BibT_eX]

[DOI]

CoRR, 2013

Non-parametric Power-law Data Clustering.

[BibT_eX]

[DOI]

Yiling Zeng

CoRR, 2013

Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network.

[BibT_eX]

[DOI]

CoRR, 2013

Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup Correlations.

[BibT_eX]

[DOI]

Richard Yi Da Xu

CoRR, 2013

2012

Model the complex dependence structures of financial variables by using canonical vine.

[BibT_eX]

[DOI]

Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

Maximum margin clustering on evolutionary data.

[BibT_eX]

[DOI]

Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 2012

A Theoretical Framework of the Graph Shift Algorithm.

[BibT_eX]

[DOI]