Yujia Zheng (郑雨嘉)

I am an MS student at CMU, advised by Prof. Kun Zhang. I completed my BS from UESTC. 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.

I will continue my graduate study (PhD) at the same group (CMU-CLeaR) as a third-year student soon.

Email  /  Google Scholar  /  LinkedIn

Documentation, GitHub

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.

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.

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.

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

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.

We aim to show the identifiability of nonlinear ICA with unconditional priors under specific conditions on the mixing process, such as independent influences.

We propose an end-to-end framework based on task-aware brain connectivity DAG structure generation for fMRI analysis.

We introduce several strategies to improve the scalability of exact score-based methods in the linear Gaussian setting with theoretical guarantees under weaker assumptions.

We present a novel lightweight recommendation paradigm that allows a well-trained recommender to be customized for arbitrary device-specific memory constraints without retraining.

We explore meta-learning to alleviate cold-start problem without any kind of side information.

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

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

We propose a multimedia dance video automatic scoring framework.

We propose a real-time fall detection method for the elderly.

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.

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.

We model user-item interactions as a heterogeneous graph which consists of various edge types.

We introduce Inter-session Item-level Interactions into Session-based Recommendation.

Aug. 2021 - Present , Carnegie Mellon University ,

MS, Logic, Computation & Methodology

Aug. 2017 - June. 2021 , University of Electronic Science and Technology of China ,

BS, Software Engineering, GPA: 3.96/4.0.

Aug. 2019 - Dec. 2019 , University of California, Berkeley ,

Visiting Student, Computational Approaches to Human Learning (CAHL) Research Lab, GPA: 4.0/4.0

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

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

Neural Information Processing Systems (NeurIPS).

International Conference on Machine Learning (ICML).

International Joint Conference on Artificial Intelligence (IJCAI).

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).

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).

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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