91 citations · 158 across the 18 of their papers we have counts for
20 papers
Efficient Temporal Point Processes via Monotone Alternating Splines
Cheng Wan, Quyu Kong, Feng Zhou
Temporal point processes (TPPs) have widespread applications across various domains. Compared to modeling the conditional intensity of a TPP, modeling its cumulative conditional in…
Long-range Modeling and Processing of Multimodal Event Sequences
Jichu Li, Yilun Zhong, Zhiting Li +2
Temporal point processes (TPPs) have emerged as powerful tools for modeling asynchronous event sequences. While recent advances have extended TPPs to handle textual information, ex…
Fair Bayesian Data Selection via Generalized Discrepancy Measures
Yixuan Zhang, Jiabin Luo, Zhenggang Wang +2
Fairness concerns are increasingly critical as machine learning models are deployed in high-stakes applications. While existing fairness-aware methods typically intervene at the mo…
Negative Binomial Variational Autoencoders for Overdispersed Latent Modeling
Yixuan Zhang, Jinhao Sheng, Wenxin Zhang +2
Although artificial neural networks are often described as brain-inspired, their representations typically rely on continuous activations, such as the continuous latent variables i…
TPP-SD: Accelerating Transformer Point Process Sampling with Speculative Decoding
Shukai Gong, Yiyang Fu, Fengyuan Ran +2
We propose TPP-SD, a novel approach that accelerates Transformer temporal point process (TPP) sampling by adapting speculative decoding (SD) techniques from language models. By ide…
DanmakuTPPBench: A Multi-modal Benchmark for Temporal Point Process Modeling and Understanding
Yue Jiang, Jichu Li, Yang Liu +3
We introduce DanmakuTPPBench, a comprehensive benchmark designed to advance multi-modal Temporal Point Process (TPP) modeling in the era of Large Language Models (LLMs). While TPPs…
Byte-token Enhanced Language Models for Temporal Point Processes Analysis
Quyu Kong, Yixuan Zhang, Yang Liu +3
Temporal Point Processes (TPPs) have been widely used for modeling event sequences on the Web, such as user reviews, social media posts, and online transactions. However, tradition…
Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approaches
Feng Zhou, Quyu Kong, Jie Qiao +3
Temporal point processes (TPPs) are stochastic process models used to characterize event sequences occurring in continuous time. Traditional statistical TPPs have a long-standing h…
Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes
Yixuan Zhang, Quyu Kong, Feng Zhou
In this study, we propose a novel deep spatio-temporal point process model, Deep Kernel Mixture Point Processes (DKMPP), that incorporates multimodal covariate information. DKMPP i…
Heterogeneous Multi-Task Gaussian Cox Processes
Feng Zhou, Quyu Kong, Zhijie Deng +3
This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g., classification and regression, via mu…
Interval-censored Transformer Hawkes: Detecting Information Operations using the Reaction of Social Systems
Quyu Kong, Pio Calderon, Rohit Ram +2
Social media is being increasingly weaponized by state-backed actors to elicit reactions, push narratives and sway public opinion. These are known as Information Operations (IO). T…
Slipping to the Extreme: A Mixed Method to Explain How Extreme Opinions Infiltrate Online Discussions
Quyu Kong, Emily Booth, Francesco Bailo +2
Qualitative research provides methodological guidelines for observing and studying communities and cultures on online social media platforms. However, such methods demand considera…
Nonlinear Hawkes Processes in Time-Varying System
Feng Zhou, Quyu Kong, Yixuan Zhang +2
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena. Although the classic Hawkes processes cover a wide range of…
Contact Tracing: Computational Bounds, Limitations and Implications
Quyu Kong, Manuel Garcia-Herranz, Ivan Dotu +1
Contact tracing has been extensively studied from different perspectives in recent years. However, there is no clear indication of why this intervention has proven effective in som…
Birdspotter: A Tool for Analyzing and Labeling Twitter Users
Rohit Ram, Quyu Kong, Marian-Andrei Rizoiu
The impact of online social media on societal events and institutions is profound; and with the rapid increases in user uptake, we are just starting to understand its ramifications…
Evently: Modeling and Analyzing Reshare Cascades with Hawkes Processes
Quyu Kong, Rohit Ram, Marian-Andrei Rizoiu
Modeling online discourse dynamics is a core activity in understanding the spread of information, both offline and online, and emergent online behavior. There is currently a discon…
Describing and Predicting Online Items with Reshare Cascades via Dual Mixture Self-exciting Processes
Quyu Kong, Marian-Andrei Rizoiu, Lexing Xie
It is well-known that online behavior is long-tailed, with most cascaded actions being short and a few being very long. A prominent drawback in generative models for online events…
Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models
Quyu Kong, Marian-Andrei Rizoiu, Lexing Xie
Epidemic models and self-exciting processes are two types of models used to describe diffusion phenomena online and offline. These models were originally developed in different sci…
Will This Video Go Viral? Explaining and Predicting the Popularity of Youtube Videos
Quyu Kong, Marian-Andrei Rizoiu, Siqi Wu +1
What makes content go viral? Which videos become popular and why others don't? Such questions have elicited significant attention from both researchers and industry, particularly i…
SIR-Hawkes: Linking Epidemic Models and Hawkes Processes to Model Diffusions in Finite Populations
Marian-Andrei Rizoiu, Swapnil Mishra, Quyu Kong +2
Among the statistical tools for online information diffusion modeling, both epidemic models and Hawkes point processes are popular choices. The former originate from epidemiology,…