Ph.D. Candidate
Department of Computer Science and Engineering
The Chinese University of Hong Kong
Email: yqchen [at] cse (dot) cuhk (dot) edu (dot) hk
Email / Github / Publications / LinkedInI am currently a Ph.D. candidate in CSE department at CUHK, advised by Prof. James Cheng. Previously, I obtained my B.Eng. degree from HIT, and enjoyed one year wonderful internship at Microsoft Research Asia.
My research spans causal learning and reasoning for the new theoretical and practical foundations of modern machine learning, with a focus on problems/data from industrial and scientific practice.
I am always open for possible collaborations, and visiting opportunities, please do not hesitate to contact me if you are interested! Please also consider giving me feedback via the anoymouns form : )
How Interpretable Are Interpretable Graph Neural Networks?
In International Conference on Machine Learning (ICML), 2024.Discovery of the Hidden World with Large Language Models
In arXiv 2402.03941, 2024.Do CLIPs Always Generalize Better than ImageNet Models?
In arXiv 2403.11497, 2024.Enhancing Evolving Domain Generalization through Dynamic Latent Representations
In Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), 2024. Oral presentation (2.2%).Understanding and Improving Feature Learning for Out-of-Distribution Generalization
In Advances in Neural Information Processing Systems (NeurIPS), 2023.Does Invariant Graph Learning via Environment Augmentation Learn Invariance?
In Advances in Neural Information Processing Systems (NeurIPS), 2023.Towards Out-of-Distribution Generalizable Predictions of Chemical Kinetics Properties
In NeurIPS Workshop on AI for Science Oral presentation, 2023.Pareto Invariant Risk Minimization: Towards Mitigating the Optimization Dilemma in OOD Generalization
In International Conference on Learning Representations (ICLR), 2023. Oral presentation at ICLR DG. Presented at ICML PODS, 2022, with Kaiwen Zhou*.Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs
In Advances in Neural Information Processing Systems (NeurIPS), 2022. Spotlight presentation (5.2%). Presented at ICML SCIS 2022. 🔥SOTA results in GOOD Benchmark🔥.Understanding and Improving Graph Injection Attack by Promoting Unnoticeability
In International Conference on Learning Representations (ICLR), 2022.DataFun Causal Inference Summit -- Graph and Causality, Virtual, October 2023.
GBA Young Scientist Gathering, Virtual, May 2023.
AI Time PhD Workshop on ICLR 2023, Virtual, Mar 2023.
Invited Talk to Causality Seminar, Virtual, December 2022.
AI Time PhD Workshop on NeurIPS 2022, Virtual, November 2022.
Invited Talk to Prof. Yiping Ke's Group at NTU, Virtual, June 2022.
AI Time PhD Workshop on ICLR 2022, Virtual, June 2022.
NeurIPS Scholar Award 2023
Top Reviewer in NeurIPS 2022, 2023
Outstanding Reviewer in ICML 2022
Outstanding Undergraduate Thesis 2020
Zuguang Ma Scholarship (< 0.1%) 2019
National Scholarship (twice, 1%) 2018, 2019
Bronze Medal, The 2018 ACM-ICPC Asia-East Continent Final 2018
Chair: NeurIPS'22 Session chair
ML Conference referee: ICML 2022/23/24, NeurIPS 2022/23/24 & DB Track 2022/23/24, ICLR 2024, AISTATS 2024, UAI 2024, AAAI 2024, IJCAI 2024, LoG 2023, CLeaR 2024
Other Conference referee: CVPR 2024, SIGKDD 2022, VLDB 2022
Journal referee: T-PAMI, TMLR, TKDD, Neural Networks, TNNLS, Pattern Recognition
Workshop referee: ICML SCIS 2023, NeurIPS CRL 2023, ICLR DG 2023