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Yujia Zheng (郑雹昉)

I am a PhD student at CMU, fortunately advised by Prof. Kun Zhang. I previously interned at UC Berkeley and NLPR, and was a summer fellow at EPFL. 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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Pinned Project
causal-learn

Causal-learn is an open-source Python package for causal discovery. 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 lead coordinator. This project is under active development, and any comments or suggestions are welcome.

Documentation, GitHub, Paper

Highlighted Publications

arXiv Local Causal Discovery with Linear non-Gaussian Cyclic Models
Haoyue Dai, Ignavier Ng, Yujia Zheng, Zhengqing Gao, Kun Zhang
AISTATS 2024

We present a general, unified local causal discovery method with linear non-Gaussian models, whether they are cyclic or acyclic.

arXiv A Versatile Causal Discovery Framework to Allow Causally-Related Hidden Variables
Xinshuai Dong*, Biwei Huang*, Ignavier Ng, Xiangchen Song, Yujia Zheng, Songyao Jin, Roberto Legaspi, Peter Spirtes, Kun Zhang
ICLR 2024, arXiv

We develop a Rank-based Latent Causal Discovery algorithm, RLCD, that can efficiently locate hidden variables, determine their cardinalities, and discover the entire causal structure over both measured and hidden ones.

arXiv Causal-learn: Causal Discovery in Python
Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang
JMLR, 2024, arXiv

We introduce causal-learn, an open-source Python library for causal discovery. We also briefly summarize different categories of methods in causal discovery and their use cases.

arXiv Generalizing Nonlinear ICA Beyond Structural Sparsity
Yujia Zheng, Kun Zhang
NeurIPS 2023, Oral (AR: 0.6%), arXiv

We establish a set of new identifiability results of nonlinear ICA in the general settings of undercompleteness, partial sparsity and source dependence, and flexible grouping structures.

arXiv On the Identifiability of Sparse ICA without Assuming Non-Gaussianity
Ignavier Ng*, Yujia Zheng*, Xinshuai Dong, Kun Zhang
NeurIPS 2023, link

We develop an identifiability theory that relies on second-order statistics without imposing further preconditions on the distribution of sources, by introducing novel assumptions on the connective structure from sources to observed variables.

arXiv 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.

arXiv On the Identifiability of Nonlinear ICA: Sparsity and Beyond
Yujia Zheng, Ignavier Ng, Kun Zhang
NeurIPS 2022, arXiv

We prove the identifiability of nonlinear ICA with assumptions only on the mixing process, such as Structural Sparsity.

ICML Partial Identifiability for Domain Adaptation
Lingjing Kong, Shaoan Xie, Weiran Yao, Yujia Zheng, Guangyi Chen, Petar Stojanov, Victor Akinwande, Kun Zhang
ICML 2022, Spotlight, arXiv

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.

NeurIPS 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.


Other Publications

arXiv Source Free Unsupervised Graph Domain Adaptation
Haitao Mao, Lun Du, Yujia Zheng, Qiang Fu, Zelin Li, Xu Chen, Shi Han, Dongmei Zhang
WSDM 2024, Best Paper Award (Honorable Mention), 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.

arXiv Whole Page Unbiased Learning to Rank
Haitao Mao, Lixin Zou, Yujia Zheng, Jiliang Tang, Xiaokai Chu, Jiashu Zhao, Dawei Yin
TheWebConf 2024, Oral, 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.

arXiv Deep DAG Learning of Effective Brain Connectivity 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, Link

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.

arXiv 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.

KDD 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.

AAAI 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.

RecSys Long-tail Session-based Recommendation
Siyi Liu, Yujia Zheng (corresponding author)
RecSys 2020, arXiv

We introduce Long-tail Recommendation into Session-based Recommendation.

ICDM DGTN: Dual-channel Graph Transition Network for Session-based Recommendation
Yujia Zheng, Siyi Liu, Zekun Li, Shu Wu
ICDM 2020 NeuRec, Long Presentation, arXiv

We model the cross-session transitions via a channel-aware graph neural network.

ACAI 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.

IGTA 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.


Preprints

arXiv Causal Representation Learning from Multiple Distributions: A General Setting
Kun Zhang, Shaoan Xie, Ignavier Ng, Yujia Zheng
Under review, arXiv

We explore a general, completely nonparametric setting of causal representation learning from multiple distributions (arising from heterogeneous data or nonstationary time series), without assuming hard interventions behind distribution changes.

arXiv Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data
Mugariya Farooq*, Shahad Hardan*, Aigerim Zhumbhayeva, Yujia Zheng, Preslav Nakov, Kun Zhang
Under review, arXiv

We investigate how various perturbations in the genome can affect the survival of patients diagnosed with breast cancer with causal principles.

arXiv 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.

arXiv 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.



Organizational Activities

Publicity Chair of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022).

Local Arrangements Chair of the 39th Conference on Uncertainty in Artificial Intelligence (UAI 2023).

Area Chair/Senior Program Committee

International Conference on Learning Representations (ICLR), Tiny Papers Track.

Reviewer/Program Committee

Neural Information Processing Systems (NeurIPS).

International Conference on Machine Learning (ICML).

International Conference on Learning Representations (ICLR).

Uncertainty in Artificial Intelligence (UAI).

International Conference on Artificial Intelligence and Statistics (AISTATS).

Causal Learning and Reasoning (CLeaR).

International Joint Conference on Artificial Intelligence (IJCAI).

AAAI Conference on Artificial Intelligence (AAAI).

The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR).

Empirical Methods in Natural Language Processing (EMNLP).

The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD).

The ACM Web Conference (TheWebConf ).

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).

SIAM International Conference on Data Mining (SDM)

The International AAAI Conference on Web and Social Media (ICWSM).

Transactions on Machine Learning Research (TMLR).

IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

IEEE Transactions on Knowledge and Data Engineering (TKDE).

IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

IEEE Transactions on Big Data (TBD).

IEEE Transactions on Information Systems (TOIS).

Awards and Fellowships

WSDM 2024 Best Paper Award (Honorable Mention).

NeurIPS 2023 Scholar Award.

Member of the final winning team in the NeurIPS 2022 CausalML Challenge. Ranked the 1st in the competition (the 1st, 1st, 1st, and 2nd, in the four tasks, respectively).

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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