Kuang, Kun

57 publications

ICML 2025 Advancing Personalized Learning with Neural Collapse for Long-Tail Challenge Hanglei Hu, Yingying Guo, Zhikang Chen, Sen Cui, Fei Wu, Kun Kuang, Min Zhang, Bo Jiang

ICML 2025 Arrow: Accelerator for Time Series Causal Discovery with Time Weaving Yuanyuan Yao, Yuan Dong, Lu Chen, Kun Kuang, Ziquan Fang, Cheng Long, Yunjun Gao, Tianyi Li

ICML 2025 D-Fusion: Direct Preference Optimization for Aligning Diffusion Models with Visually Consistent Samples Zijing Hu, Fengda Zhang, Kun Kuang

ICCV 2025 Decoding Correlation-Induced Misalignment in the Stable Diffusion Workflow for Text-to-Image Generation Yunze Tong, Fengda Zhang, Didi Zhu, Jun Xiao, Kun Kuang

ICML 2025 ERICT: Enhancing Robustness by Identifying Concept Tokens in Zero-Shot Vision Language Models Xinpeng Dong, Min Zhang, Didi Zhu, Ye Jun Jian, Zhang Keli, Aimin Zhou, Fei Wu, Kun Kuang

AAAI 2025 FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning Zhonghua Jiang, Jimin Xu, Shengyu Zhang, Tao Shen, Jiwei Li, Kun Kuang, Haibin Cai, Fei Wu

ICML 2025 Generalizing Causal Effects from Randomized Controlled Trials to Target Populations Across Diverse Environments Baohong Li, Yingrong Wang, Anpeng Wu, Ming Ma, Ruoxuan Xiong, Kun Kuang

ICML 2025 Latent Score-Based Reweighting for Robust Classification on Imbalanced Tabular Data Yunze Tong, Fengda Zhang, Zihao Tang, Kaifeng Gao, Kai Huang, Pengfei Lyu, Jun Xiao, Kun Kuang

AAAI 2025 Learning Causal Transition Matrix for Instance-Dependent Label Noise Jiahui Li, Tai-Wei Chang, Kun Kuang, Ximing Li, Long Chen, Jun Zhou

NeurIPS 2025 MS-Bench: Evaluating LMMs in Ancient Manuscript Study Through a Dunhuang Case Study Yuqing Zhang, Yue Han, Shuanghe Zhu, Haoxiang Wu, Hangqi Li, Shengyu Zhang, Junchi Yan, Zemin Liu, Kun Kuang, Huaiyong Dou, Yongquan Zhang, Fei Wu

AAAI 2025 MergeNet: Knowledge Migration Across Heterogeneous Models, Tasks, and Modalities Kunxi Li, Tianyu Zhan, Kairui Fu, Shengyu Zhang, Kun Kuang, Jiwei Li, Zhou Zhao, Fan Wu, Fei Wu

NeurIPS 2025 Mix Data or Merge Models? Balancing the Helpfulness, Honesty, and Harmlessness of Large Language Model via Model Merging Jinluan Yang, Dingnan Jin, Anke Tang, Li Shen, Didi Zhu, Zhengyu Chen, Ziyu Zhao, Daixin Wang, Qing Cui, Zhiqiang Zhang, Jun Zhou, Fei Wu, Kun Kuang

AAAI 2025 Optimize Incompatible Parameters Through Compatibility-Aware Knowledge Integration Zheqi Lv, Keming Ye, Zishu Wei, Qi Tian, Shengyu Zhang, Wenqiao Zhang, Wenjie Wang, Kun Kuang, Tat-Seng Chua, Fei Wu

ICML 2025 Rethinking Causal Ranking: A Balanced Perspective on Uplift Model Evaluation Minqin Zhu, Zexu Sun, Ruoxuan Xiong, Anpeng Wu, Baohong Li, Caizhi Tang, Jun Zhou, Fei Wu, Kun Kuang

CVPR 2025 Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards Zijing Hu, Fengda Zhang, Long Chen, Kun Kuang, Jiahui Li, Kaifeng Gao, Jun Xiao, Xin Wang, Wenwu Zhu

ICML 2024 A Generative Approach for Treatment Effect Estimation Under Collider Bias: From an Out-of-Distribution Perspective Baohong Li, Haoxuan Li, Anpeng Wu, Minqin Zhu, Shiyuan Peng, Qingyu Cao, Kun Kuang

ICLR 2024 AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillation Zihao Tang, Zheqi Lv, Shengyu Zhang, Yifan Zhou, Xinyu Duan, Fei Wu, Kun Kuang

AAAI 2024 CGMGM: A Cross-Gaussian Mixture Generative Model for Few-Shot Semantic Segmentation Junao Shen, Kun Kuang, Jiaheng Wang, Xinyu Wang, Tian Feng, Wei Zhang

AAAI 2024 Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation Minqin Zhu, Anpeng Wu, Haoxuan Li, Ruoxuan Xiong, Bo Li, Xiaoqing Yang, Xuan Qin, Peng Zhen, Jiecheng Guo, Fei Wu, Kun Kuang

AAAI 2024 CoreRec: A Counterfactual Correlation Inference for Next Set Recommendation Kexin Li, Chengjiang Long, Shengyu Zhang, Xudong Tang, Zhichao Zhai, Kun Kuang, Jun Xiao

AAAI 2024 De-Biased Attention Supervision for Text Classification with Causality Yiquan Wu, Yifei Liu, Ziyu Zhao, Weiming Lu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang

CVPR 2024 Distributionally Generative Augmentation for Fair Facial Attribute Classification Fengda Zhang, Qianpei He, Kun Kuang, Jiashuo Liu, Long Chen, Chao Wu, Jun Xiao, Hanwang Zhang

NeurIPSW 2024 Estimating Treatment Effect Across Heterogeneous Data Sources: An Instrumental Variable Approach Haotian Wang, Haoxuan Li, Wenjing Yang, Hao Zou, Wanrong Huang, Kun Kuang

ICML 2024 InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks Xueyu Hu, Ziyu Zhao, Shuang Wei, Ziwei Chai, Qianli Ma, Guoyin Wang, Xuwu Wang, Jing Su, Jingjing Xu, Ming Zhu, Yao Cheng, Jianbo Yuan, Jiwei Li, Kun Kuang, Yang Yang, Hongxia Yang, Fei Wu

ICML 2024 Learning Causal Relations from Subsampled Time Series with Two Time-Slices Anpeng Wu, Haoxuan Li, Kun Kuang, Zhang Keli, Fei Wu

ICML 2024 Learning Shadow Variable Representation for Treatment Effect Estimation Under Collider Bias Baohong Li, Haoxuan Li, Ruoxuan Xiong, Anpeng Wu, Fei Wu, Kun Kuang

AAAI 2024 Learning to Reweight for Generalizable Graph Neural Network Zhengyu Chen, Teng Xiao, Kun Kuang, Zheqi Lv, Min Zhang, Jinluan Yang, Chengqiang Lu, Hongxia Yang, Fei Wu

ICLR 2024 MetaCoCo: A New Few-Shot Classification Benchmark with Spurious Correlation Min Zhang, Haoxuan Li, Fei Wu, Kun Kuang

ICML 2024 Model Tailor: Mitigating Catastrophic Forgetting in Multi-Modal Large Language Models Didi Zhu, Zhongyisun Sun, Zexi Li, Tao Shen, Ke Yan, Shouhong Ding, Chao Wu, Kun Kuang

ICML 2024 Two-Stage Shadow Inclusion Estimation: An IV Approach for Causal Inference Under Latent Confounding and Collider Bias Baohong Li, Anpeng Wu, Ruoxuan Xiong, Kun Kuang

ICML 2023 Causal Structure Learning for Latent Intervened Non-Stationary Data Chenxi Liu, Kun Kuang

ICLR 2023 Fairness-Aware Contrastive Learning with Partially Annotated Sensitive Attributes Fengda Zhang, Kun Kuang, Long Chen, Yuxuan Liu, Chao Wu, Jun Xiao

NeurIPS 2023 HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception Junkun Yuan, Xinyu Zhang, Hao Zhou, Jian Wang, Zhongwei Qiu, Zhiyin Shao, Shaofeng Zhang, Sifan Long, Kun Kuang, Kun Yao, Junyu Han, Errui Ding, Lanfen Lin, Fei Wu, Jingdong Wang

AAAI 2023 Learning Chemical Rules of Retrosynthesis with Pre-Training Yinjie Jiang, Ying Wei, Fei Wu, Zhengxing Huang, Kun Kuang, Zhihua Wang

AAAI 2023 Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu

AAAI 2023 Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang

ICCV 2023 MAP: Towards Balanced Generalization of IID and OOD Through Model-Agnostic Adapters Min Zhang, Junkun Yuan, Yue He, Wenbin Li, Zhengyu Chen, Kun Kuang

ICML 2023 Stable Estimation of Heterogeneous Treatment Effects Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu

NeurIPS 2023 Two Heads Are Better than One: A Simple Exploration Framework for Efficient Multi-Agent Reinforcement Learning Jiahui Li, Kun Kuang, Baoxiang Wang, Xingchen Li, Fei Wu, Jun Xiao, Long Chen

ICCV 2023 Universal Domain Adaptation via Compressive Attention Matching Didi Zhu, Yinchuan Li, Junkun Yuan, Zexi Li, Kun Kuang, Chao Wu

NeurIPS 2022 ConfounderGAN: Protecting Image Data Privacy with Causal Confounder Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu

ICML 2022 Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao

NeurIPS 2022 GRASP: Navigating Retrosynthetic Planning with Goal-Driven Policy Yemin Yu, Ying Wei, Kun Kuang, Zhengxing Huang, Huaxiu Yao, Fei Wu

ICML 2022 Instrumental Variable Regression with Confounder Balancing Anpeng Wu, Kun Kuang, Bo Li, Fei Wu

ICML 2022 The Role of Deconfounding in Meta-Learning Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu, Ying Wei

ICMLW 2022 Towards Multi-Level Fairness and Robustness on Federated Learning Fengda Zhang, Kun Kuang, Yuxuan Liu, Long Chen, Jiaxun Lu, Yunfeng Shao, Fei Wu, Chao Wu, Jun Xiao

CVPRW 2021 DeVLBert: Out-of-Distribution Visio-Linguistic Pretraining with Causality Shengyu Zhang, Tan Jiang, Tan Wang, Kun Kuang, Zhou Zhao, Jianke Zhu, Jin Yu, Hongxia Yang, Fei Wu

ICML 2021 Explainable Automated Graph Representation Learning with Hyperparameter Importance Xin Wang, Shuyi Fan, Kun Kuang, Wenwu Zhu

CVPRW 2021 Grounded, Controllable and Debiased Image Completion with Lexical Semantics Shengyu Zhang, Tan Jiang, Qinghao Huang, Ziqi Tan, Kun Kuang, Zhou Zhao, Siliang Tang, Jin Yu, Hongxia Yang, Yi Yang, Fei Wu

AAAI 2021 Judgment Prediction via Injecting Legal Knowledge into Neural Networks Leilei Gan, Kun Kuang, Yi Yang, Fei Wu

ICCV 2021 Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples with Graph-Based Virtual Labels Jiannan Guo, Haochen Shi, Yangyang Kang, Kun Kuang, Siliang Tang, Zhuoren Jiang, Changlong Sun, Fei Wu, Yueting Zhuang

AAAI 2021 Stable Adversarial Learning Under Distributional Shifts Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin

IJCAI 2020 Decorrelated Clustering with Data Selection Bias Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi, Jiawei Liu, Bai Wang

AAAI 2020 Stable Learning via Sample Reweighting Zheyan Shen, Peng Cui, Tong Zhang, Kun Kuang

AAAI 2020 Stable Prediction with Model Misspecification and Agnostic Distribution Shift Kun Kuang, Ruoxuan Xiong, Peng Cui, Susan Athey, Bo Li

ICML 2019 Disentangled Graph Convolutional Networks Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, Wenwu Zhu

AAAI 2017 Treatment Effect Estimation with Data-Driven Variable Decomposition Kun Kuang, Peng Cui, Bo Li, Meng Jiang, Shiqiang Yang, Fei Wang