13 citations · 17 across the 16 of their papers we have counts for
16 papers
Poisson-Gamma Dynamical Systems with Time-varying Transition Dynamics
Jiahao Wang, Yijun Wang, Nan Fang +1
Bayesian methodologies for handling count-valued time series have gained prominence due to their ability to infer interpretable latent structures and to estimate uncertainties. Amo…
Poisson-Gamma Modeling of Inter-Relational Dependencies in Dynamic Knowledge Graphs
Nan Fang, Yijun Wang, Hao Liao +1
Dynamic knowledge graphs are ubiquitous in today's AI applications, as we represent molecular structures, social relationships, and language information using these graph models. A…
CauchyNet: Compact and Data-Efficient Learning using Holomorphic Activation Functions
Hong-Kun Zhang, Xin Li, Sikun Yang +1
A novel neural network inspired by Cauchy's integral formula, is proposed for function approximation tasks that include time series forecasting, missing data imputation, etc. Hence…
Conformalized Exceptional Model Mining: Telling Where Your Model Performs (Not) Well
Xin Du, Sikun Yang, Wouter Duivesteijn +1
Understanding the nuanced performance of machine learning models is essential for responsible deployment, especially in high-stakes domains like healthcare and finance. This paper…
Hierarchical-Graph-Structured Edge Partition Models for Learning Evolving Community Structure
Xincan Yu, Sikun Yang
We propose a novel dynamic network model to capture evolving latent communities within temporal networks. To achieve this, we decompose each observed dynamic edge between vertices…
On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction
Xin Jing, Yichen Jing, Yuhuan Lu +3
Information popularity prediction is important yet challenging in various domains, including viral marketing and news recommendations. The key to accurately predicting information…
Adaptive Gradient Regularization: A Faster and Generalizable Optimization Technique for Deep Neural Networks
Huixiu Jiang, Ling Yang, Yu Bao +2
Stochastic optimization plays a crucial role in the advancement of deep learning technologies. Over the decades, significant effort has been dedicated to improving the training eff…
Scaling up Dynamic Edge Partition Models via Stochastic Gradient MCMC
Sikun Yang, Heinz Koeppl
The edge partition model (EPM) is a generative model for extracting an overlapping community structure from static graph-structured data. In the EPM, the gamma process (GaP) prior…
Negative-Binomial Randomized Gamma Markov Processes for Heterogeneous Overdispersed Count Time Series
Rui Huang, Sikun Yang, Heinz Koeppl
Modeling count-valued time series has been receiving increasing attention since count time series naturally arise in physical and social domains. Poisson gamma dynamical systems (P…
A Poisson-Gamma Dynamic Factor Model with Time-Varying Transition Dynamics
Jiahao Wang, Sikun Yang, Heinz Koeppl +3
Probabilistic approaches for handling count-valued time sequences have attracted amounts of research attentions because their ability to infer explainable latent structures and to…
A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs
Sikun Yang, Hongyuan Zha
Continuously-observed event occurrences, often exhibit self- and mutually-exciting effects, which can be well modeled using temporal point processes. Beyond that, these event dynam…
Learning Stochastic Dynamical Systems as an Implicit Regularization with Graph Neural Networks
Jin Guo, Ting Gao, Yufu Lan +3
Stochastic Gumbel graph networks are proposed to learn high-dimensional time series, where the observed dimensions are often spatially correlated. To that end, the observed randomn…
Estimating Latent Population Flows from Aggregated Data via Inversing Multi-Marginal Optimal Transport
Sikun Yang, Hongyuan Zha
We study the problem of estimating latent population flows from aggregated count data. This problem arises when individual trajectories are not available due to privacy issues or m…
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks
Sikun Yang, Heinz Koeppl
We propose a novel probabilistic framework to model continuous-time interaction events data. Our goal is to infer the \emph{implicit} community structure underlying the temporal in…
Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis
Sikun Yang, Heinz Koeppl
Group factor analysis (GFA) methods have been widely used to infer the common structure and the group-specific signals from multiple related datasets in various fields including sy…
A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks
Sikun Yang, Heinz Koeppl
We present a probabilistic model for learning from dynamic relational data, wherein the observed interactions among networked nodes are modeled via the Bernoulli Poisson link funct…