hiltcanadian.blogg.se - Tao jun kun do

Personalized Latent Structure Learning for Recommendation, TPAMI, 2023. Shengyu Zhang, Fuli Feng, Kun Kuang*, Wenqiao Zhang, Zhou Zhao, Hongxia Yang, Tat-Seng Chua, Fei Wu. ML-LJP: Multi-Law Aware Legal Judgment Prediction, SIGIR, 2023. Yifei Liu, Yiquan Wu, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, Kun Kuang*. Federated Mutual Learning: A Collaborative Machine Learning Method for Heterogeneous Data,Models, and Objectives, FITEE, 2023. Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Zheqi Lv, Kun Kuang, Chao Wu, Fei Wu. Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization, TKDE, 2023. Junkun Yuan, Xu Ma, Defang Chen, Fei Wu, Lanfen Lin, Kun Kuang*. Instrumental Variable-Driven Domain Generalization with Unobserved Confounders, TKDD, 2023. Junkun Yuan, Xu Ma, Ruoxuan Xiong, Mingming Gong, Fei Wu, Lanfen Lin, Kun Kuang*.

Causal Structure Learning for Latent Intervened Non-stationary Data, ICML, 2023. Stable Estimation of Heterogeneous Treatment Effect, ICML, 2023.Ĭhenxi Liu, Kun Kuang*. Focus-aware Response Generation in Inquiry Conversation, Findings of ACL, 2023.Īnpeng Wu, Kun Kuang*, Ruoxuan Xiong, Bo Li, Fei Wu. Yiquan Wu, Weiming Lu, Yating Zhang, Adam Jatowt, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang*. LK-IB: A Hybrid Framework with Legal Knowledge Injection for Compulsory Measure Prediction, AI and Law, 2023. Xiang Zhou, Qi Liu, Yiquan Wu, Qiangchao Chen, Kun Kuang*. Who should be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation, KDD, 2023. Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, Peng Cui. Treatment Effect Estimation with Adjustment Feature Selection, KDD, 2023. Haotian Wang, Kun Kuang, Haoang Chi, Longqi Yang, Mingyang Geng, Wanrong Huang, Wenjing Yang. Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization, KDD, 2023. Yunze Tong, Junkun Yuan, Min Zhang, Didi Zhu, Keli Zhang, Fei Wu, Kun Kuang*. Instrumental Variables in Causal Inference and Machine Learning: A Survey, arxiv, 2022. I gave a tutorial on Causally Regularized Machine Learning in PAKDD 2019, together with Peng Cui and Bo Li.Īnpeng Wu, Kun Kuang*, Ruoxuan Xiong, Fei Wu. I gave a tutorial on Causal Inference and Stable Learning in ICME 2019, together with Peng Cui and Bo Li. I gave a invited talk on Causal Inference in Observational Studies in MLMI 2021. I gave a invited talk on Causal Inference with Instrumental Variables in PIPC 2022. One paper was accepted by IJCV on Domain Generalization. Three papers were accepted by AAAI 2023. One paper was accepted by ICLR 2023 on Fairness-aware Contrastive Learning. Two papers were accepted by TheWebConf 2023. One paper was accepted by TPAMI on Causal Recommendation. One paper was accepted by SIGIR 2023 on Legal Judgment Prediction. One paper was accepted by TKDE on Federated Domain Generalization. One paper was accepted by TKDD on Instrumental Variable-Driven Domain Generalization. Two papers were accepted by ICML on Causal Inference and Structure Learning.

We published a survey paper on Instrumental Variables here, and develop a toolkit of IVs methods.

students and Interns! If interested, please contact me with your detailed CV! News We are always actively recruiting highly motivated Postdocs, Ph.D. In particular, I am interested in promoting the convergence of causal inference and machine learning, including improving the effectiveness of causal inference with machine learning technologies, and bringing stability and interpretability of machine learning with causal inference technologies. My research interests include causal inference, machine learning, and data mining. Susan Athey's group at Stanford University as a visiting student. in the Department of Computer Science and Technology at Tsinghua University in 2019, coadvised by Prof. I'm an Associate Professor of the College of Computer Science and Technology at Zhejiang University. Office: Room 108, Zetong Building, Yuquan Campus College of Computer Science and Technology