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2020 – today
- 2023
[j20]Zhao Ziyu
, Kun Kuang, Bo Li, Peng Cui, Runze Wu, Jun Xiao, Fei Wu:
Differentiated matching for individual and average treatment effect estimation. Data Min. Knowl. Discov. 37(1): 205-227 (2023)- [j19]Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin:
Domain-Specific Bias Filtering for Single Labeled Domain Generalization. Int. J. Comput. Vis. 131(2): 552-571 (2023) - [j18]Fengda Zhang, Kun Kuang, Long Chen, Zhaoyang You, Tao Shen, Jun Xiao, Yin Zhang, Chao Wu, Fei Wu, Yueting Zhuang, Xiaolin Li:
Federated unsupervised representation learning. Frontiers Inf. Technol. Electron. Eng. 24(8): 1181-1193 (2023) - [j17]Tao Shen, Jie Zhang, Xinkang Jia, Fengda Zhang, Zheqi Lv, Kun Kuang, Chao Wu, Fei Wu:
Federated mutual learning: a collaborative machine learning method for heterogeneous data, models, and objectives. Frontiers Inf. Technol. Electron. Eng. 24(10): 1390-1402 (2023) - [j16]Shengyu Zhang, Fuli Feng, Kun Kuang, Wenqiao Zhang, Zhou Zhao, Hongxia Yang, Tat-Seng Chua, Fei Wu:
Personalized Latent Structure Learning for Recommendation. IEEE Trans. Pattern Anal. Mach. Intell. 45(8): 10285-10299 (2023) - [j15]Junkun Yuan, Xu Ma, Ruoxuan Xiong, Mingming Gong, Xiangyu Liu, Fei Wu, Lanfen Lin, Kun Kuang:
Instrumental Variable-Driven Domain Generalization with Unobserved Confounders. ACM Trans. Knowl. Discov. Data 17(8): 118:1-118:21 (2023) - [j14]Anpeng Wu, Junkun Yuan, Kun Kuang, Bo Li, Runze Wu, Qiang Zhu, Yueting Zhuang, Fei Wu:
Learning Decomposed Representations for Treatment Effect Estimation. IEEE Trans. Knowl. Data Eng. 35(5): 4989-5001 (2023) - [j13]Kun Kuang, Haotian Wang, Yue Liu, Ruoxuan Xiong, Runze Wu, Weiming Lu, Yueting Zhuang, Fei Wu, Peng Cui, Bo Li:
Stable Prediction With Leveraging Seed Variable. IEEE Trans. Knowl. Data Eng. 35(6): 6392-6404 (2023) - [j12]Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang:
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI. IEEE Trans. Knowl. Data Eng. 35(7): 6866-6886 (2023) - [j11]Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li:
Distributionally Robust Learning With Stable Adversarial Training. IEEE Trans. Knowl. Data Eng. 35(11): 11288-11300 (2023) - [j10]Junkun Yuan, Xu Ma, Defang Chen, Fei Wu, Lanfen Lin, Kun Kuang:
Collaborative Semantic Aggregation and Calibration for Federated Domain Generalization. IEEE Trans. Knowl. Data Eng. 35(12): 12528-12541 (2023) - [c70]Yinjie Jiang, Ying Wei, Fei Wu, Zhengxing Huang, Kun Kuang, Zhihua Wang:
Learning Chemical Rules of Retrosynthesis with Pre-training. AAAI 2023: 5113-5121 - [c69]Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu:
Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation. AAAI 2023: 10324-10332 - [c68]Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang:
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning. AAAI 2023: 11672-11680 - [c67]Yiquan Wu, Weiming Lu, Yating Zhang, Adam Jatowt, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang:
Focus-aware Response Generation in Inquiry Conversation. ACL (Findings) 2023: 12585-12599 - [c66]Kun Kuang:
Causal Inspired Trustworthy Machine Learning. ACM TUR-C 2023: 3-4 - [c65]Shanqi Liu, Yujing Hu, Runze Wu, Dong Xing, Yu Xiong, Changjie Fan, Kun Kuang, Yong Liu:
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning. AAMAS 2023: 31-39 - [c64]Fengda Zhang, Kun Kuang, Long Chen, Yuxuan Liu, Chao Wu, Jun Xiao:
Fairness-aware Contrastive Learning with Partially Annotated Sensitive Attributes. ICLR 2023 - [c63]Chenxi Liu, Kun Kuang:
Causal Structure Learning for Latent Intervened Non-stationary Data. ICML 2023: 21756-21777 - [c62]Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu:
Stable Estimation of Heterogeneous Treatment Effects. ICML 2023: 37496-37510 - [c61]Haoxuan Li, Chunyuan Zheng, Peng Wu, Kun Kuang, Yue Liu, Peng Cui:
Who Should Be Given Incentives? Counterfactual Optimal Treatment Regimes Learning for Recommendation. KDD 2023: 1235-1247 - [c60]Yunze Tong, Junkun Yuan, Min Zhang, Didi Zhu, Keli Zhang, Fei Wu, Kun Kuang:
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization. KDD 2023: 2189-2200 - [c59]Haotian Wang, Kun Kuang, Haoang Chi, Longqi Yang, Mingyang Geng, Wanrong Huang, Wenjing Yang:
Treatment Effect Estimation with Adjustment Feature Selection. KDD 2023: 2290-2301 - [c58]Thuc Duy Le, Jiuyong Li, Robert Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui, Kun Kuang, Jian Pei, Fei Wang, Mattia Prosperi:
Preface: The 2023 ACM SIGKDD Workshop on Causal Discovery, Prediction and Decision. CDPD 2023: 1-2 - [c57]Shengyu Zhang, Yunze Tong, Kun Kuang, Fuli Feng, Jiezhong Qiu, Jin Yu, Zhou Zhao, Hongxia Yang, Zhongfei Zhang, Fei Wu:
Stable Prediction on Graphs with Agnostic Distribution Shifts. CDPD 2023: 49-74 - [c56]Tianqi Zhao, Ming Kong, Tian Liang, Qiang Zhu, Kun Kuang, Fei Wu:
CLAP: Contrastive Language-Audio Pre-training Model for Multi-modal Sentiment Analysis. ICMR 2023: 622-626 - [c55]Didi Zhu, Yinchuan Li, Yunfeng Shao, Jianye Hao, Fei Wu, Kun Kuang, Jun Xiao, Chao Wu:
Generalized Universal Domain Adaptation with Generative Flow Networks. ACM Multimedia 2023: 8304-8315 - [c54]Yifei Liu, Yiquan Wu, Yating Zhang, Changlong Sun, Weiming Lu, Fei Wu, Kun Kuang:
ML-LJP: Multi-Law Aware Legal Judgment Prediction. SIGIR 2023: 1023-1034 - [c53]Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang, Kun Kuang, Yan Zhang, Yulin Kang, Jun Zhou:
Graph Neural Network with Two Uplift Estimators for Label-Scarcity Individual Uplift Modeling. WWW 2023: 395-405 - [c52]Zheqi Lv, Wenqiao Zhang, Shengyu Zhang, Kun Kuang, Feng Wang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Beng Chin Ooi, Fei Wu:
DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization. WWW 2023: 3077-3085 - [e1]Thuc Duy Le, Jiuyong Li, Robert Ness, Sofia Triantafillou, Shohei Shimizu, Peng Cui, Kun Kuang, Jian Pei, Fei Wang, Mattia Prosperi:
The KDD'23 Workshop on Causal Discovery, Prediction and Decision, 07 August 2023, Long Beach, CA, USA. Proceedings of Machine Learning Research 218, PMLR 2023 [contents] - [i58]Shanqi Liu, Yujing Hu, Runze Wu, Dong Xing, Yu Xiong, Changjie Fan, Kun Kuang, Yong Liu:
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning. CoRR abs/2302.06872 (2023) - [i57]Zheqi Lv, Zhengyu Chen, Shengyu Zhang, Kun Kuang, Wenqiao Zhang, Mengze Li, Beng Chin Ooi, Fei Wu:
IDEAL: Toward High-efficiency Device-Cloud Collaborative and Dynamic Recommendation System. CoRR abs/2302.07335 (2023) - [i56]Didi Zhu, Yinchuan Li, Junkun Yuan, Zexi Li, Yunfeng Shao, Kun Kuang, Chao Wu:
Universal Domain Adaptation via Compressive Attention Matching. CoRR abs/2304.11862 (2023) - [i55]Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Fei Wu, Kun Kuang:
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction. CoRR abs/2304.14770 (2023) - [i54]Didi Zhu, Yinchuan Li, Yunfeng Shao, Jianye Hao, Fei Wu, Kun Kuang, Jun Xiao, Chao Wu:
Generalized Universal Domain Adaptation with Generative Flow Networks. CoRR abs/2305.04466 (2023) - [i53]Yunze Tong, Junkun Yuan, Min Zhang, Didi Zhu, Keli Zhang, Fei Wu, Kun Kuang:
Quantitatively Measuring and Contrastively Exploring Heterogeneity for Domain Generalization. CoRR abs/2305.15889 (2023) - [i52]Didi Zhu, Yinchuan Li, Min Zhang, Junkun Yuan, Jiashuo Liu, Zexi Li, Kun Kuang, Chao Wu:
Bridging the Gap: Neural Collapse Inspired Prompt Tuning for Generalization under Class Imbalance. CoRR abs/2306.15955 (2023) - [i51]Anpeng Wu, Haoxuan Li, Kun Kuang, Keli Zhang, Fei Wu:
Hierarchical Topological Ordering with Conditional Independence Test for Limited Time Series. CoRR abs/2308.08148 (2023) - [i50]Junao Shen, Long Chen, Kun Kuang, Fei Wu, Tian Feng, Wei Zhang:
MEDOE: A Multi-Expert Decoder and Output Ensemble Framework for Long-tailed Semantic Segmentation. CoRR abs/2308.08213 (2023) - [i49]Chengyuan Liu, Fubang Zhao, Lizhi Qing, Yangyang Kang, Changlong Sun, Kun Kuang, Fei Wu:
A Chinese Prompt Attack Dataset for LLMs with Evil Content. CoRR abs/2309.11830 (2023) - [i48]Yiquan Wu, Siying Zhou, Yifei Liu, Weiming Lu, Xiaozhong Liu, Yating Zhang, Changlong Sun, Fei Wu, Kun Kuang:
Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaboration. CoRR abs/2310.09241 (2023) - [i47]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:
HAP: Structure-Aware Masked Image Modeling for Human-Centric Perception. CoRR abs/2310.20695 (2023) - 2022
- [j9]Ming Kong, Qing Guo, Shuowen Zhou, Mengze Li, Kun Kuang, Zhengxing Huang, Fei Wu, Xiaohong Chen, Qiang Zhu:
Attribute-aware interpretation learning for thyroid ultrasound diagnosis. Artif. Intell. Medicine 131: 102344 (2022) - [j8]Bin Wei, Kun Kuang, Changlong Sun, Jun Feng, Yating Zhang, Xinli Zhu, Jianghong Zhou, Yinsheng Zhai, Fei Wu:
A full-process intelligent trial system for smart court. Frontiers Inf. Technol. Electron. Eng. 23(2): 186-206 (2022) - [j7]Kun Kuang, Hengtao Zhang, Runze Wu, Fei Wu, Yueting Zhuang, Aijun Zhang:
Balance-Subsampled Stable Prediction Across Unknown Test Data. ACM Trans. Knowl. Discov. Data 16(3): 45:1-45:21 (2022) - [j6]Junkun Yuan, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, Lanfen Lin:
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition. ACM Trans. Knowl. Discov. Data 16(4): 74:1-74:20 (2022) - [j5]Kun Kuang, Peng Cui, Hao Zou, Bo Li, Jianrong Tao, Fei Wu, Shiqiang Yang:
Data-Driven Variable Decomposition for Treatment Effect Estimation. IEEE Trans. Knowl. Data Eng. 34(5): 2120-2134 (2022) - [c51]Leilei Gan, Yuxian Meng, Kun Kuang, Xiaofei Sun, Chun Fan, Fei Wu, Jiwei Li:
Dependency Parsing as MRC-based Span-Span Prediction. ACL (1) 2022: 2427-2437 - [c50]Yemin Yu, Kun Kuang, Jiangchao Yang, Zeke Wang, Kunyang Jia, Weiming Lu, Hongxia Yang, Fei Wu:
Multi-objective Meta-return Reinforcement Learning for Sequential Recommendation. CICAI (2) 2022: 95-111 - [c49]Siying Zhou, Yifei Liu, Yiquan Wu, Kun Kuang, Chunyan Zheng, Fei Wu:
Similar Case Based Prison Term Prediction. CICAI (3) 2022: 284-297 - [c48]Tianqi Zhao, Ming Kong, Kun Kuang, Zhengxing Huang, Qiang Zhu, Fei Wu:
Connecting Patients with Pre-diagnosis: A Multiple Graph Regularized Method for Mental Disorder Diagnosis. CICAI (2) 2022: 362-374 - [c47]Yiquan Wu, Yifei Liu, Weiming Lu, Yating Zhang, Jun Feng, Changlong Sun, Fei Wu, Kun Kuang:
Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework. EMNLP 2022: 4787-4799 - [c46]Chengyuan Liu, Leilei Gan, Kun Kuang, Fei Wu:
Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples. EMNLP 2022: 5499-5512 - [c45]Zhengyu Chen, Teng Xiao, Kun Kuang:
BA-GNN: On Learning Bias-Aware Graph Neural Network. ICDE 2022: 3012-3024 - [c44]Yuxuan Si, Zhengqing Fang, Kun Kuang, Zhengxing Huang, Yu-Feng Yao, Fei Wu:
Disentangled Sequential Autoencoder with Local Consistency for Infectious Keratitis Diagnosis. ICIP 2022: 3893-3897 - [c43]Yinjie Jiang, Zhengyu Chen, Kun Kuang, Luotian Yuan, Xinhai Ye, Zhihua Wang, Fei Wu, Ying Wei:
The Role of Deconfounding in Meta-learning. ICML 2022: 10161-10176 - [c42]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Changjie Fan, Fei Wu, Jun Xiao:
Deconfounded Value Decomposition for Multi-Agent Reinforcement Learning. ICML 2022: 12843-12856 - [c41]Anpeng Wu, Kun Kuang, Bo Li, Fei Wu:
Instrumental Variable Regression with Confounder Balancing. ICML 2022: 24056-24075 - [c40]Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu:
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? KDD 2022: 1183-1191 - [c39]Haotian Wang, Wenjing Yang, Longqi Yang, Anpeng Wu, Liyang Xu, Jing Ren, Fei Wu, Kun Kuang:
Estimating Individualized Causal Effect with Confounded Instruments. KDD 2022: 1857-1867 - [c38]Jiannan Guo, Yangyang Kang, Yu Duan, Xiaozhong Liu, Siliang Tang, Wenqiao Zhang, Kun Kuang, Changlong Sun, Fei Wu:
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning. KDD 2022: 2935-2945 - [c37]Ming Kong, Zhengxing Huang, Kun Kuang, Qiang Zhu, Fei Wu:
TranSQ: Transformer-Based Semantic Query for Medical Report Generation. MICCAI (8) 2022: 610-620 - [c36]Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin:
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization. ACM Multimedia 2022: 2361-2370 - [c35]Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu:
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder. NeurIPS 2022 - [c34]Yemin Yu, Ying Wei, Kun Kuang, Zhengxing Huang, Huaxiu Yao, Fei Wu:
GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy. NeurIPS 2022 - [c33]Ziqi Tan, Shengyu Zhang, Nuanxin Hong, Kun Kuang, Yifan Yu, Jin Yu, Zhou Zhao, Hongxia Yang, Shiyuan Pan, Jingren Zhou, Fei Wu:
Uncovering Causal Effects of Online Short Videos on Consumer Behaviors. WSDM 2022: 997-1006 - [i46]Shaohua Fan, Xiao Wang, Chuan Shi, Kun Kuang, Nian Liu, Bai Wang:
Debiased Graph Neural Networks with Agnostic Label Selection Bias. CoRR abs/2201.07708 (2022) - [i45]Jiannan Guo, Yangyang Kang, Yu Duan, Xiaozhong Liu, Siliang Tang, Wenqiao Zhang, Kun Kuang, Changlong Sun, Fei Wu:
Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning. CoRR abs/2206.03288 (2022) - [i44]Shuang Luo, Yinchuan Li, Jiahui Li, Kun Kuang, Furui Liu, Yunfeng Shao, Chao Wu:
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning? CoRR abs/2206.11054 (2022) - [i43]Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin:
Label-Efficient Domain Generalization via Collaborative Exploration and Generalization. CoRR abs/2208.03644 (2022) - [i42]Zheqi Lv, Feng Wang, Shengyu Zhang, Kun Kuang, Hongxia Yang, Fei Wu:
Personalizing Intervened Network for Long-tailed Sequential User Behavior Modeling. CoRR abs/2208.09130 (2022) - [i41]Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Minqing Zhu, Yuxuan Liu, Bo Li, Furui Liu, Zhihua Wang, Fei Wu:
Treatment Effect Estimation with Unmeasured Confounders in Data Fusion. CoRR abs/2208.10912 (2022) - [i40]Zheqi Lv, Feng Wang, Kun Kuang, Yongwei Wang, Zhengyu Chen, Tao Shen, Hongxia Yang, Fei Wu:
MetaNetwork: A Task-agnostic Network Parameters Generation Framework for Improving Device Model Generalization. CoRR abs/2209.05227 (2022) - [i39]Qiaowei Miao, Junkun Yuan, Kun Kuang:
Domain Generalization via Contrastive Causal Learning. CoRR abs/2210.02655 (2022) - [i38]Chengyuan Liu, Leilei Gan, Kun Kuang, Fei Wu:
Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples. CoRR abs/2210.08548 (2022) - [i37]Ziyu Zhao, Kun Kuang, Ruoxuan Xiong, Fei Wu:
Learning Individual Treatment Effects under Heterogeneous Interference in Networks. CoRR abs/2210.14080 (2022) - [i36]Leilei Gan, Baokui Li, Kun Kuang, Yi Yang, Fei Wu:
Exploiting Contrastive Learning and Numerical Evidence for Improving Confusing Legal Judgment Prediction. CoRR abs/2211.08238 (2022) - [i35]Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Bo Li, Fei Wu:
Confounder Balancing for Instrumental Variable Regression with Latent Variable. CoRR abs/2211.10008 (2022) - [i34]Qi Tian, Kun Kuang, Furui Liu, Baoxiang Wang:
Learning From Good Trajectories in Offline Multi-Agent Reinforcement Learning. CoRR abs/2211.15612 (2022) - [i33]Qi Tian, Kun Kuang, Kelu Jiang, Furui Liu, Zhihua Wang, Fei Wu:
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder. CoRR abs/2212.01767 (2022) - [i32]Anpeng Wu, Kun Kuang, Ruoxuan Xiong, Fei Wu:
Instrumental Variables in Causal Inference and Machine Learning: A Survey. CoRR abs/2212.05778 (2022) - 2021
- [j4]Kun Kuang, Yunzhe Li, Bo Li, Peng Cui, Hongxia Yang, Jianrong Tao, Fei Wu:
Continuous treatment effect estimation via generative adversarial de-confounding. Data Min. Knowl. Discov. 35(6): 2467-2497 (2021) - [c32]Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin:
Stable Adversarial Learning under Distributional Shifts. AAAI 2021: 8662-8670 - [c31]Leilei Gan, Kun Kuang, Yi Yang, Fei Wu:
Judgment Prediction via Injecting Legal Knowledge into Neural Networks. AAAI 2021: 12866-12874 - [c30]Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, Fei Wu:
BertGCN: Transductive Text Classification by Combining GNN and BERT. ACL/IJCNLP (Findings) 2021: 1456-1462 - [c29]Zhao Ziyu, Kun Kuang, Fei Wu:
Estimating Treatment Effect via Differentiated Confounder Matching. CICAI 2021: 689-699 - [c28]Shengyu Zhang, Tan Jiang, Tan Wang, Kun Kuang, Zhou Zhao, Jianke Zhu, Jin Yu, Hongxia Yang, Fei Wu:
DeVLBert: Out-of-Distribution Visio-Linguistic Pretraining With Causality. CVPR Workshops 2021: 1744-1747 - [c27]Shengyu Zhang, Tan Jiang, Qinghao Huang, Ziqi Tan, Kun Kuang, Zhou Zhao, Siliang Tang, Jin Yu, Hongxia Yang, Yi Yang, Fei Wu:
Grounded, Controllable and Debiased Image Completion With Lexical Semantics. CVPR Workshops 2021: 1748-1751 - [c26]Jiannan Guo, Haochen Shi, Yangyang Kang, Kun Kuang, Siliang Tang, Zhuoren Jiang, Changlong Sun, Fei Wu, Yueting Zhuang:
Semi-supervised Active Learning for Semi-supervised Models: Exploit Adversarial Examples with Graph-based Virtual Labels. ICCV 2021: 2876-2885 - [c25]Xin Wang, Shuyi Fan, Kun Kuang, Wenwu Zhu:
Explainable Automated Graph Representation Learning with Hyperparameter Importance. ICML 2021: 10727-10737 - [c24]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao:
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. KDD 2021: 934-942 - [c23]Qi Tian, Kun Kuang, Kelu Jiang, Fei Wu, Yisen Wang:
Analysis and Applications of Class-wise Robustness in Adversarial Training. KDD 2021: 1561-1570 - [c22]Jiahui Li, Kun Kuang, Lin Li, Long Chen, Songyang Zhang, Jian Shao, Jun Xiao:
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation. ACM Multimedia 2021: 3664-3672 - [c21]Shengyu Zhang, Donghui Wang, Zhou Zhao, Siliang Tang, Kun Kuang, Di Xie, Fei Wu:
MGD-GAN: Text-to-Pedestrian Generation Through Multi-grained Discrimination. PRCV (2) 2021: 662-673 - [i31]Yuxiao Lin, Yuxian Meng, Xiaofei Sun, Qinghong Han, Kun Kuang, Jiwei Li, Fei Wu:
BertGCN: Transductive Text Classification by Combining GCN and BERT. CoRR abs/2105.05727 (2021) - [i30]Leilei Gan, Yuxian Meng, Kun Kuang, Xiaofei Sun, Chun Fan, Fei Wu, Jiwei Li:
Dependency Parsing as MRC-based Span-Span Prediction. CoRR abs/2105.07654 (2021) - [i29]Qi Tian, Kun Kuang, Kelu Jiang, Fei Wu, Yisen Wang:
Analysis and Applications of Class-wise Robustness in Adversarial Training. CoRR abs/2105.14240 (2021) - [i28]Jiahui Li, Kun Kuang, Baoxiang Wang, Furui Liu, Long Chen, Fei Wu, Jun Xiao:
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning. CoRR abs/2106.00285 (2021) - [i27]Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li:
Distributionally Robust Learning with Stable Adversarial Training. CoRR abs/2106.15791 (2021) - [i26]Junkun Yuan, Anpeng Wu, Kun Kuang, Bo Li, Runze Wu, Fei Wu, Lanfen Lin:
Auto IV: Counterfactual Prediction via Automatic Instrumental Variable Decomposition. CoRR abs/2107.05884 (2021) - [i25]Jiahui Li, Kun Kuang, Lin Li, Long Chen, Songyang Zhang, Jian Shao, Jun Xiao:
Instance-wise or Class-wise? A Tale of Neighbor Shapley for Concept-based Explanation. CoRR abs/2109.01369 (2021) - [i24]Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin:
Domain-Specific Bias Filtering for Single Labeled Domain Generalization. CoRR abs/2110.00726 (2021) - [i23]Junkun Yuan, Xu Ma, Kun Kuang, Ruoxuan Xiong, Mingming Gong, Lanfen Lin:
Learning Domain-Invariant Relationship with Instrumental Variable for Domain Generalization. CoRR abs/2110.01438 (2021) - [i22]Shengyu Zhang, Kun Kuang, Jiezhong Qiu, Jin Yu, Zhou Zhao, Hongxia Yang, Zhongfei Zhang, Fei Wu:
Stable Prediction on Graphs with Agnostic Distribution Shift. CoRR abs/2110.03865 (2021) - [i21]Junkun Yuan, Xu Ma, Defang Chen, Kun Kuang, Fei Wu, Lanfen Lin:
Do We Need to Directly Access the Source Datasets for Domain Generalization? CoRR abs/2110.06736 (2021) - [i20]Leilei Gan, Yating Zhang, Kun Kuang, Lin Yuan, Shuo Li, Changlong Sun, Xiaozhong Liu, Fei Wu:
Dialogue Inspectional Summarization with Factual Inconsistency Awareness. CoRR abs/2111.03284 (2021) - [i19]Fengda Zhang, Kun Kuang, Yuxuan Liu, Chao Wu, Fei Wu, Jiaxun Lu, Yunfeng Shao, Jun Xiao:
Unified Group Fairness on Federated Learning. CoRR abs/2111.04986 (2021) - [i18]Jiangchao Yao, Shengyu Zhang, Yang Yao, Feng Wang, Jianxin Ma, Jianwei Zhang, Yunfei Chu, Luo Ji, Kunyang Jia, Tao Shen, Anpeng Wu, Fengda Zhang, Ziqi Tan, Kun Kuang, Chao Wu, Fei Wu, Jingren Zhou, Hongxia Yang:
Edge-Cloud Polarization and Collaboration: A Comprehensive Survey. CoRR abs/2111.06061 (2021) - [i17]Qi Tian, Kun Kuang, Baoxiang Wang, Furui Liu, Fei Wu:
Multi-agent Communication with Graph Information Bottleneck under Limited Bandwidth. CoRR abs/2112.10374 (2021) - 2020
- [j3]Fashen Li, Lian Li, Jianping Yin, Liang Huang, Qingguo Zhou, Ning An, Yong Zhang, Li Liu, Jialin Zhang, Kun Kuan