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Currently, I am a Ph.D. student at Texas A&M University, supervised by Prof. Xiaoning Qian. My research interests include but are not limited to causal reasoning, transfer learning and data mining. Before that, I obtained the master's (supervised by Prof. Fan Zhou and Prof. Ting Zhong) and bachelor's degrees in School of Information and Software Engineering from University of Electronic Science and Technology of China (UESTC) in 2023 and 2020, repectively.Email: {FirstName}{LastName} AT tamu DOT edu / Google Scholar / Github |
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DyCVAE: Learning Dynamic Causal Factors for Non-stationary Series Domain Generalization (Student Abstract) Weifeng Zhang, Zhiyuan Wang, Kunpeng Zhang, Ting Zhong, Fan Zhou AAAI 2023. Area: Domain Generalization, Representation Learning, and Disentanglement. Learning domain-invariant representations is a major task of out-of-distribution generalization, extending existing generalization methods for adapting non-stationary time series may be ineffective, because they fail to model the underlying causal factors due to temporal-domain shifts except for source-domain shifts. To this end, we propose a novel model DyCVAE to learn dynamic causal factors. |
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Learning Latent Seasonal-Trend Representations for Time Series Forecasting Zhiyuan Wang, Xovee Xu, Weifeng Zhang, Goce Trajcevski, Ting Zhong, Fan Zhou NeurIPS 2022. Area: Time Series Forecasting, Representation Learning, and Disentanglement. Motivated by the success of disentangled variational autoencoder in computer vision and classical time series decomposition, we propose LaST that infers a couple of representations that depict seasonal and trend components of time series. Extensive experiments demonstrates its superiority on the time series forecasting task. |
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CausalRD: A causal view of Rumor Detection via Eliminating Popularity and Conformity Biases[Link] Weifeng Zhang, Ting Zhong, Ce Li, Kunpeng Zhang, Fan Zhou INFOCOM 2022. Area: Social Network, Representation Learning, and Causal Inference. We provide a new view of rumor detection through causality, which aims at eliminating popularity and conformity biases in social network. |
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Identifying IP Usage Scenarios: Problems, Data, and Benchmarks[Link] Fan Zhou, Weifeng Zhang, Yong Wang, Ting Zhong, Goce Trajcevski, Ashfaq Khokhar IEEE Network. Area: Network, Resource. We presented and investigated a novel and practical problem called IP usage scenario prediction in the internet community. |
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Last updated on Aug. 17th, 2023
Template credit to Jon Barron
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