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I am an incoming PhD student at CMU, fortunately advised by Prof. Kun Zhang. My research interests lie primarily in the linear span of causality and machine learning. Currently, I mainly study the following topics: causal discovery, causal representation learning, and structure learning. I am also interested in deep learning from a causal view. |
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causal-learn: Causal Discovery for Python
Causal-learn is a Python translation and extension of the Tetrad java code. It offers the implementations of up-to-date causal discovery methods as well as simple and intuitive APIs. Causal-learn is contributed by several groups and I am the major coordinator. This project is under active development, and any comments or suggestions are welcome. |
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Generalized Precision Matrix for Scalable Estimation of Nonparametric Markov Networks
Yujia Zheng, Ignavier Ng, Yewen Fan, Kun Zhang ICLR 2023, arXiv We generalize the characterization of the conditional independence structure to handle general distributions for all data types, thus giving rise to a Markov network structure learning algorithm in one of the most general settings. |
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Deep Structure Learning on Brain Networks for fMRI Analysis
Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang ISBI 2023 We propose a brain network generation approach via modeling the connections among ROIs as DAGs to identify effective brain connectivities and predict the target in an end-to-end manner. |
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On the Identifiability of Nonlinear ICA: Sparsity and Beyond
Yujia Zheng, Ignavier Ng, Kun Zhang NeurIPS 2022, arXiv ICLR 2022 OSC, Oral We prove the identifiability of nonlinear ICA with assumptions only on the mixing process, such as Structural Sparsity. |
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Partial Disentanglement for Domain Adaptation
Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang ICML 2022, Link We show that under reasonable assumptions on the data generating process, as well as leveraging the principle of minimality, we can obtain partial identifiability of the changing and invariant parts of the generating process. |
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Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural Networks
Yue Yu, Xuan Kan, Hejie Cui, Ran Xu, Yujia Zheng, Xiangchen Song, Yanqiao Zhu, Kun Zhang, Razieh Nabi, Ying Guo, Chao Zhang, Carl Yang IEEE BigData 2022 BrainNN, arXiv We propose an end-to-end framework based on task-aware brain connectivity DAG structure generation for fMRI analysis. |
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Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions
Ignavier Ng, Yujia Zheng, Jiji Zhang, Kun Zhang NeurIPS 2021, arXiv We introduce several strategies to improve the scalability of exact score-based methods in the linear Gaussian setting with theoretical guarantees under weaker assumptions. |
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Learning Elastic Embeddings for Customizing On-Device Recommenders
Tong Chen, Hongzhi Yin, Yujia Zheng, Zi Huang, Yang Wang, Meng Wang KDD 2021, Oral, arXiv We present a novel lightweight recommendation paradigm that allows a well-trained recommender to be customized for arbitrary device-specific memory constraints without retraining. |
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Cold-start Sequential Recommendation via Meta Learner
Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu AAAI 2021, arXiv We explore meta-learning to alleviate cold-start problem without any kind of side information. |
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Long-tail Session-based Recommendation
Siyi Liu, Yujia Zheng (corresponding author) RecSys 2020, arXiv We introduce Long-tail Recommendation into Session-based Recommendation. |
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DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu ICDM 2020 NeuRec, Long Oral, arXiv We model the cross-session transitions via a channel-aware graph neural network. |
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ADS: Multimedia Dance Video Automatic Scoring Framework Based on Transfer Learning
Yujia Zheng ACAI 2020, Link We propose a multimedia dance video automatic scoring framework. |
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ReFall: Real-time Fall Detection of Continuous Depth Maps with RFD-Net
Yujia Zheng, Siyi Liu, Zairong Wang, Yunbo Rao IGTA 2019, Link We propose a real-time fall detection method for the elderly. |
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Whole Page Unbiased Learning to Rank
Haitao Mao, Lixin Zou, Yujia Zheng, Jiliang Tang, Xiaokai Chu, Jiashu Zhao, Dawei Yin Under review, arXiv We introduce a new task, namely whole-page unbiased learning to rank, and propose a novel framework to automatically discover and mitigate the biases in an end-to-end manner. |
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Source Free Unsupervised Graph Domain Adaptation
Haitao Mao, Lun Du, Yujia Zheng, Qiang Fu, Zelin Li, Xu Chen, Shi Han, Dongmei Zhang Under review, arXiv We propose a new scenario named source free unsupervised graph domain adaptation. In this scenario, the only information we can leverage from the source domain is the well-trained source model, without any exposure to the source graph and its labels. |
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Heterogenous Graph Collaborative Filtering
Zekun Li*, Yujia Zheng*, Shu Wu, Xiaoyu Zhang, Liang Wang Preprint, arXiv We model user-item interactions as a heterogeneous graph which consists of various edge types. |
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Balancing Multi-level Interactions for Session-based Recommendation
Yujia Zheng* (corresponding author), Siyi Liu*, Zailei Zhou Preprint, arXiv We introduce Inter-session Item-level Interactions into Session-based Recommendation. |
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Aug. 2021 - May. 2023 , Carnegie Mellon University , MS, Logic, Computation & Methodology |
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Aug. 2017 - June. 2021 , University of Electronic Science and Technology of China , BS, Software Engineering, GPA: 3.96/4.0. |
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Aug. 2019 - Dec. 2019 , University of California, Berkeley , Visiting Student, Computational Approaches to Human Learning (CAHL) Research Lab, GPA: 4.0/4.0 |
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Feb. 2019 - June. 2019 , National Taiwan University of Science and Technology , Exchange Student, Image/Video Processing and Multimedia Lab (IVPML), GPA: 4.0/4.0 |
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Publicity Co-chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022). |
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International Conference on Learning Representations (ICLR), Tiny Papers Track. |
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Neural Information Processing Systems (NeurIPS). International Conference on Machine Learning (ICML). International Joint Conference on Artificial Intelligence (IJCAI). Uncertainty in Artificial Intelligence (UAI). AAAI Conference on Artificial Intelligence (AAAI). The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). The ACM Web Conference (WWW). The ACM Web Search and Data Mining Conference (WSDM). European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Learning on Graphs Conference (LoG). Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). The International AAAI Conference on Web and Social Media (ICWSM). IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). IEEE Transactions on Neural Networks and Learning Systems (TNNLS). IEEE Transactions on Big Data (TBD). IEEE Transactions on Information Systems TOIS (TOIS). |
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Member of the youth delegation in the 73rd session of the UN General Assembly (UNGA 73). Member of the final winning team in the NeurIPS 2022 CausalML Challenge: Causal Insights for Learning Paths in Education competition. NeurIPS 2022 Scholar Award. NeurIPS 2022 Top Reviewer. Summer Research Fellowship at EPFL (<2%). Top 1% in multiple data competitions. Tang Lixin Scholarship. |
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