Xiaoqian Wang (王小倩)
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Assistant Professor 516 Northwestern Ave Email: joywang@purdue.edu |
About Me
My research interests are generally trustworthy machine learning. I am particularly interested in designing novel machine learning models to improve interpretability, fairness, and robustness. I also work on the intersection of machine learning and bioinformatics, healthcare.
I obtained my Ph.D. degree from the University of Pittsburgh in 2019, and my advisor is Prof. Heng Huang. Prior to this, I received my bachelor degree from Zhejiang University in 2013.
I am looking for highly self-motivated Ph.D. students to work on emerging challenges in Machine/Deep Learning and/or Healthcare. Please send me your CV and transcripts if you are interested.
News
2023: One papers accepted at CVPR 2023. Congratulations to Taeuk!
2022: One papers accepted at AAAI 2023. Congratulations to my wonderful collaborators!
2022: Three papers accepted at NeurIPS 2022. Congratulations to Junyi, Yipei, Taeuk!
2022: Honored to receive an NSF CAREER award!
2022: Invited to serve as a Senior Program Committee Member for AAAI 2023
2022: One paper accepted at ICML 2022. Congratulations to Junyi!
2021: One paper accepted at AAAI 2022. Congratulations to Taeuk!
2021: One paper accepted at NeurIPS 2021. Congratulations to Yipei!
Students
Taeuk Jang (PhD student, 2019-)
Yipei Wang (PhD student, 2020-)
Junyi Chai (PhD student, 2021-)
Shenyu Lu (PhD student, 2021-)
Hoin Jung (PhD student, 2023-)
Recent Publications
Difficulty-based Sampling for Debiased Contrastive Representation Learning
Taeuk Jang, and Xiaoqian Wang
In Proceedings of the 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2023)
Accepted to Appear.
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
Tianci Liu, Haoyu Wang, Yaqing Wang, Xiaoqian Wang, Lu Su, and Jing Gao
In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI 2023)
Distinguished Paper Award.
Why Not Other Classes?: Towards Class-Contrastive Back-Propagation Explanations
Yipei Wang, and Xiaoqian Wang
In the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
Self-Supervised Fair Representation Learning without Demographics
Junyi Chai, and Xiaoqian Wang
In the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
Fairness without Demographics through Knowledge Distillation
Junyi Chai, Taeuk Jang, and Xiaoqian Wang
In the Thirty-Sixth Conference on Neural Information Processing Systems (NeurIPS 2022)
Fairness with Adaptive Weights
Junyi Chai, and Xiaoqian Wang
In Proceedings of the 39th International Conference on Machine Learning (ICML 2022)
Group-Aware Threshold Adaptation for Fair Classification
Taeuk Jang, Pengyi Shi, and Xiaoqian Wang
In Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022)
Acceptance rate: 15.0% (1349/9020)
Self-Interpretable Model with Transformation Equivariant Interpretation
Yipei Wang, and Xiaoqian Wang
In Proceedings of the Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS 2021)
Constructing a Fair Classifier with Generated Fair Data
Taeuk Jang, Feng Zheng, and Xiaoqian Wang
In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2021)
Shapley Explanation Networks
Rui Wang, Xiaoqian Wang, and David Inouye
In the Ninth International Conference on Learning Representations (ICLR 2021)
On the Convergence of Stochastic Compositional Gradient Descent Ascent Method
Hongchang Gao, Xiaoqian Wang, Lei Luo, and Xinghua Shi
In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI 2021)
Acceptance rate: 13.9% (587/4284)