25 citations · 136 across the 18 of their papers we have counts for
18 papers
Task-Driven Causal Feature Distillation: Towards Trustworthy Risk Prediction
Zhixuan Chu, Mengxuan Hu, Qing Cui +2
Since artificial intelligence has seen tremendous recent successes in many areas, it has sparked great interest in its potential for trustworthy and interpretable risk prediction.…
Monotonic Neural Ordinary Differential Equation: Time-series Forecasting for Cumulative Data
Zhichao Chen, Leilei Ding, Zhixuan Chu +3
Time-Series Forecasting based on Cumulative Data (TSFCD) is a crucial problem in decision-making across various industrial scenarios. However, existing time-series forecasting meth…
Trustworthy Representation Learning Across Domains
Ronghang Zhu, Dongliang Guo, Daiqing Qi +3
As AI systems have obtained significant performance to be deployed widely in our daily live and human society, people both enjoy the benefits brought by these technologies and suff…
Leveraging Large Language Models for Pre-trained Recommender Systems
Zhixuan Chu, Hongyan Hao, Xin Ouyang +9
Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectivel…
Continual Learning in Predictive Autoscaling
Hongyan Hao, Zhixuan Chu, Shiyi Zhu +7
Predictive Autoscaling is used to forecast the workloads of servers and prepare the resources in advance to ensure service level objectives (SLOs) in dynamic cloud environments. Ho…
Enhancing Recommender Systems with Large Language Model Reasoning Graphs
Yan Wang, Zhixuan Chu, Xin Ouyang +10
Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behavior…
EasyTPP: Towards Open Benchmarking Temporal Point Processes
Siqiao Xue, Xiaoming Shi, Zhixuan Chu +9
Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point…
pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting
Yunyi Zhou, Zhixuan Chu, Yijia Ruan +3
Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the…
Continual Causal Inference with Incremental Observational Data
Zhixuan Chu, Ruopeng Li, Stephen Rathbun +1
The era of big data has witnessed an increasing availability of observational data from mobile and social networking, online advertising, web mining, healthcare, education, public…
Causal Effect Estimation: Recent Advances, Challenges, and Opportunities
Zhixuan Chu, Jianmin Huang, Ruopeng Li +2
Causal inference has numerous real-world applications in many domains, such as health care, marketing, political science, and online advertising. Treatment effect estimation, a fun…
Fair Attribute Completion on Graph with Missing Attributes
Dongliang Guo, Zhixuan Chu, Sheng Li
Tackling unfairness in graph learning models is a challenging task, as the unfairness issues on graphs involve both attributes and topological structures. Existing work on fair gra…
Continual Causal Effect Estimation: Challenges and Opportunities
Zhixuan Chu, Sheng Li
A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising…
Hierarchical Capsule Prediction Network for Marketing Campaigns Effect
Zhixuan Chu, Hui Ding, Guang Zeng +4
Marketing campaigns are a set of strategic activities that can promote a business's goal. The effect prediction for marketing campaigns in a real industrial scenario is very comple…
Multi-Task Adversarial Learning for Treatment Effect Estimation in Basket Trials
Zhixuan Chu, Stephen L. Rathbun, Sheng Li
Estimating treatment effects from observational data provides insights about causality guiding many real-world applications such as different clinical study designs, which are the…
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data
Zhixuan Chu, Stephen Rathbun, Sheng Li
The foremost challenge to causal inference with real-world data is to handle the imbalance in the covariates with respect to different treatment options, caused by treatment select…
Graph Infomax Adversarial Learning for Treatment Effect Estimation with Networked Observational Data
Zhixuan Chu, Stephen L. Rathbun, Sheng Li
Treatment effect estimation from observational data is a critical research topic across many domains. The foremost challenge in treatment effect estimation is how to capture hidden…
Matching in Selective and Balanced Representation Space for Treatment Effects Estimation
Zhixuan Chu, Stephen L. Rathbun, Sheng Li
The dramatically growing availability of observational data is being witnessed in various domains of science and technology, which facilitates the study of causal inference. Howeve…
A Survey on Causal Inference
Liuyi Yao, Zhixuan Chu, Sheng Li +3
Causal inference is a critical research topic across many domains, such as statistics, computer science, education, public policy and economics, for decades. Nowadays, estimating c…