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ORCIDPeng Cui 0001
Person information
- affiliation (PhD 2010): Tsinghua University, Department of Computer Science and Technology, Beijing, China
- not to be confused with: Peng Cui 0007
Other persons with the same name
- Peng Cui — disambiguation page
- Peng Cui 0002
![[0000-0001-9168-170X]](https://dblp.org/img/orcid-mark.12x12.png "0000-0001-9168-170X")
— Northwestern Polytechnical University, School of Marine Science and Technology, Xi'an, China - Peng Cui 0003 — Shanghai Jiao Tong University, School of Life Science and Biotechnology / Shanghai Center for Bioinformation Technology, China
- Peng Cui 0004 — Chinese Academy of Agricultural Sciences, Agricultural Genomics Institute at Shenzhen, China (and 3 more)
- Peng Cui 0005 — Shandong University, School of Control Science and Engineering, Jinan, China
- Peng Cui 0006 — Harbin Institute of Technology, School of Computer Science and Technology, China
- Peng Cui 0007 — Tsinghua University, Department of Computer Science and Technology, BNRist, THU-Bosch Joint ML Center, Beijing, China
- Peng Cui 0008 — Renmin University of China, School of Information Resource Management, Beijing, China
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2020 – today
- 2024
[j62]Bing Yuan, Jiang Zhang, Aobo Lyu, Jiayun Wu, Zhipeng Wang, Mingzhe Yang, Kaiwei Liu, Muyun Mou, Peng Cui:
Emergence and Causality in Complex Systems: A Survey of Causal Emergence and Related Quantitative Studies. Entropy 26(2): 108 (2024)- [j61]Shaohua Fan, Xiao Wang, Chuan Shi, Peng Cui, Bai Wang:
Generalizing Graph Neural Networks on Out-of-Distribution Graphs. IEEE Trans. Pattern Anal. Mach. Intell. 46(1): 322-337 (2024) - [j60]Shixuan Liu, Changjun Fan, Kewei Cheng, Yunfei Wang, Peng Cui, Yizhou Sun, Zhong Liu:
Inductive Meta-Path Learning for Schema-Complex Heterogeneous Information Networks. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 10196-10209 (2024) - [j59]Huacheng Li, Chunhe Xia, Tianbo Wang, Zhao Wang, Peng Cui, Xiaojian Li:
GRASS: Learning Spatial-Temporal Properties From Chainlike Cascade Data for Microscopic Diffusion Prediction. IEEE Trans. Neural Networks Learn. Syst. 35(11): 16313-16327 (2024) - [c168]Jiashuo Liu, Jiayun Wu, Jie Peng, Xiaoyu Wu, Yang Zheng, Bo Li, Peng Cui:
Enhancing Distributional Stability among Sub-populations. AISTATS 2024: 2125-2133 - [c167]Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui:
Rethinking the Evaluation Protocol of Domain Generalization. CVPR 2024: 21897-21908 - [c166]Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng, Xu Chen, Peng Cui:
Debiased Collaborative Filtering with Kernel-Based Causal Balancing. ICLR 2024 - [c165]Yue He, Dongbai Li, Pengfei Tian, Han Yu, Jiashuo Liu, Hao Zou, Peng Cui:
Domain-wise Data Acquisition to Improve Performance under Distribution Shift. ICML 2024 - [c164]José H. Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu:
Stability Evaluation through Distributional Perturbation Analysis. ICML 2024 - [c163]Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui:
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications. ICML 2024 - [c162]Yue He, Pengfei Tian, Renzhe Xu, Xinwei Shen, Xingxuan Zhang, Peng Cui:
Model-Agnostic Random Weighting for Out-of-Distribution Generalization. KDD 2024: 1050-1061 - [c161]Wenjing Yang, Haotian Wang, Haoxuan Li, Hao Zou, Ruochun Jin, Kun Kuang, Peng Cui:
Your Neighbor Matters: Towards Fair Decisions Under Networked Interference. KDD 2024: 3829-3840 - [c160]Qingsong Wen, Jing Liang, Carles Sierra, Rose Luckin, Richard Jiarui Tong, Zitao Liu, Peng Cui, Jiliang Tang:
AI for Education (AI4EDU): Advancing Personalized Education with LLM and Adaptive Learning. KDD 2024: 6743-6744 - [i74]Xingxuan Zhang, Jiansheng Li, Wenjing Chu, Junjia Hai, Renzhe Xu, Yuqing Yang, Shikai Guan, Jiazheng Xu, Peng Cui:
On the Out-Of-Distribution Generalization of Multimodal Large Language Models. CoRR abs/2402.06599 (2024) - [i73]Han Yu, Jiashuo Liu, Xingxuan Zhang, Jiayun Wu, Peng Cui:
A Survey on Evaluation of Out-of-Distribution Generalization. CoRR abs/2403.01874 (2024) - [i72]Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui:
PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creators. CoRR abs/2403.15524 (2024) - [i71]Haoxuan Li, Chunyuan Zheng, Yanghao Xiao, Peng Wu, Zhi Geng, Xu Chen, Peng Cui:
Debiased Collaborative Filtering with Kernel-Based Causal Balancing. CoRR abs/2404.19596 (2024) - [i70]Jose H. Blanchet, Peng Cui, Jiajin Li, Jiashuo Liu:
Stability Evaluation via Distributional Perturbation Analysis. CoRR abs/2405.03198 (2024) - [i69]Jiayun Wu, Jiashuo Liu, Peng Cui, Zhiwei Steven Wu:
Bridging Multicalibration and Out-of-distribution Generalization Beyond Covariate Shift. CoRR abs/2406.00661 (2024) - 2023
- [j58]Ziyu Zhao, 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) - [j57]Ziwei Zhang, Peng Cui, Jian Pei, Xin Wang, Wenwu Zhu:
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs. IEEE Trans. Knowl. Data Eng. 35(3): 2544-2555 (2023) - [j56]Heng Chang, Yu Rong, Tingyang Xu, Wenbing Huang, Honglei Zhang, Peng Cui, Xin Wang, Wenwu Zhu, Junzhou Huang:
Adversarial Attack Framework on Graph Embedding Models With Limited Knowledge. IEEE Trans. Knowl. Data Eng. 35(5): 4499-4513 (2023) - [j55]Ziwei Zhang, Chenhao Niu, Peng Cui, Jian Pei, Bo Zhang, Wenwu Zhu:
Permutation-Equivariant and Proximity-Aware Graph Neural Networks With Stochastic Message Passing. IEEE Trans. Knowl. Data Eng. 35(6): 6182-6193 (2023) - [j54]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) - [j53]Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, Guandong Xu:
Causal Disentanglement for Semantic-Aware Intent Learning in Recommendation. IEEE Trans. Knowl. Data Eng. 35(10): 9836-9849 (2023) - [j52]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) - [j51]Jianxin Li, Lifang He, Hao Peng, Peng Cui, Charu C. Aggarwal, Philip S. Yu:
Guest Editorial Introduction to the Special Issue on Anomaly Detection in Emerging Data-Driven Applications: Theory, Algorithms, and Applications. IEEE Trans. Knowl. Data Eng. 35(12): 11982-11983 (2023) - [j50]Xumin Chen, Ruobing Xie, Zhijie Qiu, Peng Cui, Ziwei Zhang, Shukai Liu, Shiqiang Yang, Bo Zhang, Leyu Lin:
Group-based social diffusion in recommendation. World Wide Web (WWW) 26(4): 1775-1792 (2023) - [c159]Han Yu, Peng Cui, Yue He, Zheyan Shen, Yong Lin, Renzhe Xu, Xingxuan Zhang:
Stable Learning via Sparse Variable Independence. AAAI 2023: 10998-11006 - [c158]Yue He, Xinwei Shen, Renzhe Xu, Tong Zhang, Yong Jiang, Wenchao Zou, Peng Cui:
Covariate-Shift Generalization via Random Sample Weighting. AAAI 2023: 11828-11836 - [c157]Hao Zou, Haotian Wang, Renzhe Xu, Bo Li, Jian Pei, Ye Jun Jian, Peng Cui:
Factual Observation Based Heterogeneity Learning for Counterfactual Prediction. CLeaR 2023: 350-370 - [c156]Xingxuan Zhang, Yue He, Renzhe Xu, Han Yu, Zheyan Shen, Peng Cui:
NICO++: Towards Better Benchmarking for Domain Generalization. CVPR 2023: 16036-16047 - [c155]Xingxuan Zhang, Renzhe Xu, Han Yu, Hao Zou, Peng Cui:
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization. CVPR 2023: 20247-20257 - [c154]Ke Tu, Zhengwei Wu, Binbin Hu, Zhiqiang Zhang, Peng Cui, Xiaolong Li, Jun Zhou:
A Scalable Social Recommendation Framework with Decoupled Graph Neural Network. DASFAA (4) 2023: 519-531 - [c153]Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong, Pengfei Tian, Peng Cui:
Flatness-Aware Minimization for Domain Generalization. ICCV 2023: 5166-5179 - [c152]Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, Wenwu Zhu:
Intention-aware Sequential Recommendation with Structured Intent Transition : (Extended Abstract). ICDE 2023: 3759-3760 - [c151]Jiashuo Liu, Jiayun Wu, Renjie Pi, Renzhe Xu, Xingxuan Zhang, Bo Li, Peng Cui:
Measure the Predictive Heterogeneity. ICLR 2023 - [c150]Haoxuan Li, Yanghao Xiao, Chunyuan Zheng, Peng Wu, Peng Cui:
Propensity Matters: Measuring and Enhancing Balancing for Recommendation. ICML 2023: 20182-20194 - [c149]Xiaoyu Tan, Lin Yong, Shengyu Zhu, Chao Qu, Xihe Qiu, Yinghui Xu, Peng Cui, Yuan Qi:
Provably Invariant Learning without Domain Information. ICML 2023: 33563-33580 - [c148]Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui:
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits. ICML 2023: 38674-38706 - [c147]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 - [c146]Yu Xiong, Runze Wu, Shiwei Zhao, Jianrong Tao, Xudong Shen, Tangjie Lyu, Changjie Fan, Peng Cui:
A Data-Driven Decision Support Framework for Player Churn Analysis in Online Games. KDD 2023: 5303-5314 - [c145]Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo:
Graph Neural Networks: Foundation, Frontiers and Applications. KDD 2023: 5831-5832 - [c144]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 - [c143]Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong:
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. NeurIPS 2023 - [c142]Valeria Fionda, Olaf Hartig, Reyhaneh Abdolazimi, Sihem Amer-Yahia, Hongzhi Chen, Xiao Chen, Peng Cui, Jeffrey Dalton, Xin Luna Dong, Lisette Espín-Noboa, Wenqi Fan, Manuela Fritz, Quan Gan, Jingtong Gao, Xiaojie Guo, Torsten Hahmann, Jiawei Han, Soyeon Caren Han, Estevam Hruschka, Liang Hu, Jiaxin Huang, Utkarshani Jaimini, Olivier Jeunen, Yushan Jiang, Fariba Karimi, George Karypis, Krishnaram Kenthapadi, Himabindu Lakkaraju, Hady W. Lauw, Thai Le, Trung-Hoang Le, Dongwon Lee, Geon Lee, Liat Levontin, Cheng-Te Li, Haoyang Li, Ying Li, Jay Chiehen Liao, Qidong Liu, Usha Lokala, Ben London, Siqu Long, Hande Küçük-McGinty, Yu Meng, Seungwhan Moon, Usman Naseem, Pradeep Natarajan, Behrooz Omidvar-Tehrani, Zijie Pan, Devesh Parekh, Jian Pei, Tiago Peixoto, Steven Pemberton, Josiah Poon, Filip Radlinski, Federico Rossetto, Kaushik Roy, Aghiles Salah, Mehrnoosh Sameki, Amit P. Sheth, Cogan Shimizu, Kijung Shin, Dongjin Song, Julia Stoyanovich, Dacheng Tao, Johanne Trippas, Quoc Truong, Yu-Che Tsai, Adaku Uchendu, Bram van den Akker, Lin Wang, Minjie Wang, Shoujin Wang, Xin Wang, Ingmar Weber, Henry Weld, Lingfei Wu, Da Xu, Yifan Ethan Xu, Shuyuan Xu, Bo Yang, Ke Yang, Elad Yom-Tov, Jaemin Yoo, Zhou Yu, Reza Zafarani, Hamed Zamani, Meike Zehlike, Qi Zhang, Xikun Zhang, Yongfeng Zhang, Yu Zhang, Zheng Zhang, Liang Zhao, Xiangyu Zhao, Wenwu Zhu:
Tutorials at The Web Conference 2023. WWW (Companion Volume) 2023: 648-658 - [c141]Jie Peng, Hao Zou, Jiashuo Liu, Shaoming Li, Yibao Jiang, Jian Pei, Peng Cui:
Offline Policy Evaluation in Large Action Spaces via Outcome-Oriented Action Grouping. WWW 2023: 1220-1230 - [e6]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] - [i68]Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang:
Model Agnostic Sample Reweighting for Out-of-Distribution Learning. CoRR abs/2301.09819 (2023) - [i67]Xingxuan Zhang, Renzhe Xu, Han Yu, Hao Zou, Peng Cui:
Gradient Norm Aware Minimization Seeks First-Order Flatness and Improves Generalization. CoRR abs/2303.03108 (2023) - [i66]Jiashuo Liu, Jiayun Wu, Bo Li, Peng Cui:
Predictive Heterogeneity: Measures and Applications. CoRR abs/2304.00305 (2023) - [i65]Han Yu, Xingxuan Zhang, Renzhe Xu, Jiashuo Liu, Yue He, Peng Cui:
Rethinking the Evaluation Protocol of Domain Generalization. CoRR abs/2305.15253 (2023) - [i64]Zimu Wang, Jiashuo Liu, Hao Zou, Xingxuan Zhang, Yue He, Dongxu Liang, Peng Cui:
Exploring and Exploiting Data Heterogeneity in Recommendation. CoRR abs/2305.15431 (2023) - [i63]Zheyan Shen, Han Yu, Peng Cui, Jiashuo Liu, Xingxuan Zhang, Linjun Zhou, Furui Liu:
Meta Adaptive Task Sampling for Few-Domain Generalization. CoRR abs/2305.15644 (2023) - [i62]Renzhe Xu, Haotian Wang, Xingxuan Zhang, Bo Li, Peng Cui:
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits. CoRR abs/2305.19158 (2023) - [i61]Shixuan Liu, Changjun Fan, Kewei Cheng, Yunfei Wang, Peng Cui, Yizhou Sun, Zhong Liu:
Inductive Meta-path Learning for Schema-complex Heterogeneous Information Networks. CoRR abs/2307.03937 (2023) - [i60]Jiashuo Liu, Tianyu Wang, Peng Cui, Hongseok Namkoong:
On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets. CoRR abs/2307.05284 (2023) - [i59]Xingxuan Zhang, Renzhe Xu, Han Yu, Yancheng Dong, Pengfei Tian, Peng Cui:
Flatness-Aware Minimization for Domain Generalization. CoRR abs/2307.11108 (2023) - [i58]Jiashuo Liu, Jiayun Wu, Tianyu Wang, Hao Zou, Bo Li, Peng Cui:
Geometry-Calibrated DRO: Combating Over-Pessimism with Free Energy Implications. CoRR abs/2311.05054 (2023) - [i57]Bing Yuan, Zhang Jiang, Aobo Lyu, Jiayun Wu, Zhipeng Wang, Mingzhe Yang, Kaiwei Liu, Muyun Mou, Peng Cui:
Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies. CoRR abs/2312.16815 (2023) - 2022
- [j49]Yuan Meng, Yancheng Dong, Shixuan Liu, Chaohao Yuan, Yue He, Jian Pei, Peng Cui:
Distilling Causal Metaknowledge from Knowledge Graphs. IEEE Data Eng. Bull. 45(4): 102-121 (2022) - [j48]Peng Cui, Susan Athey:
Stable learning establishes some common ground between causal inference and machine learning. Nat. Mach. Intell. 4(2): 110-115 (2022) - [j47]Ruobing Xie, Qi Liu, Shukai Liu, Ziwei Zhang, Peng Cui, Bo Zhang, Leyu Lin:
Improving Accuracy and Diversity in Matching of Recommendation With Diversified Preference Network. IEEE Trans. Big Data 8(4): 955-967 (2022) - [j46]Ziwei Zhang, Peng Cui, Wenwu Zhu:
Deep Learning on Graphs: A Survey. IEEE Trans. Knowl. Data Eng. 34(1): 249-270 (2022) - [j45]Xiao Wang, Yuanfu Lu, Chuan Shi, Ruijia Wang, Peng Cui, Shuai Mou:
Dynamic Heterogeneous Information Network Embedding With Meta-Path Based Proximity. IEEE Trans. Knowl. Data Eng. 34(3): 1117-1132 (2022) - [j44]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) - [j43]Haoyang Li, Xin Wang, Ziwei Zhang, Jianxin Ma, Peng Cui, Wenwu Zhu:
Intention-Aware Sequential Recommendation With Structured Intent Transition. IEEE Trans. Knowl. Data Eng. 34(11): 5403-5414 (2022) - [j42]Minghao Zhao, Qilin Deng, Kai Wang, Runze Wu, Jianrong Tao, Changjie Fan, Liang Chen, Peng Cui:
Bilateral Filtering Graph Convolutional Network for Multi-relational Social Recommendation in the Power-law Networks. ACM Trans. Inf. Syst. 40(2): 31:1-31:24 (2022) - [c140]Xingxuan Zhang, Linjun Zhou, Renzhe Xu, Peng Cui, Zheyan Shen, Haoxin Liu:
Towards Unsupervised Domain Generalization. CVPR 2022: 4900-4910 - [c139]Linjun Zhou, Peng Cui, Xingxuan Zhang, Yinan Jiang, Shiqiang Yang:
Adversarial Eigen Attack on BlackBox Models. CVPR 2022: 15233-15241 - [c138]Xingxuan Zhang, Yue He, Tan Wang, Jiaxin Qi, Han Yu, Zimu Wang, Jie Peng, Renzhe Xu, Zheyan Shen, Yulei Niu, Hanwang Zhang, Peng Cui:
NICO Challenge: Out-of-Distribution Generalization for Image Recognition Challenges. ECCV Workshops (6) 2022: 433-450 - [c137]Renzhe Xu, Xingxuan Zhang, Zheyan Shen, Tong Zhang, Peng Cui:
A Theoretical Analysis on Independence-driven Importance Weighting for Covariate-shift Generalization. ICML 2022: 24803-24829 - [c136]Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, Tong Zhang:
Model Agnostic Sample Reweighting for Out-of-Distribution Learning. ICML 2022: 27203-27221 - [c135]Hao Zou, Bo Li, Jiangang Han, Shuiping Chen, Xuetao Ding, Peng Cui:
Counterfactual Prediction for Outcome-Oriented Treatments. ICML 2022: 27693-27706 - [c134]Zimu Wang, Yue He, Jiashuo Liu, Wenchao Zou, Philip S. Yu, Peng Cui:
Invariant Preference Learning for General Debiasing in Recommendation. KDD 2022: 1969-1978 - [c133]Yunfei Lu, Peng Cui, Linyun Yu, Lei Li, Wenwu Zhu:
Uncovering the Heterogeneous Effects of Preference Diversity on User Activeness: A Dynamic Mixture Model. KDD 2022: 3458-3467 - [c132]Daixin Wang, Zujian Weng, Zhengwei Wu, Zhiqiang Zhang, Peng Cui, Hongwei Zhao, Jun Zhou:
A Graph Learning Based Framework for Billion-Scale Offline User Identification. KDD 2022: 4001-4009 - [c131]Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao, Xiaojie Guo:
Graph Neural Networks: Foundation, Frontiers and Applications. KDD 2022: 4840-4841 - [c130]Yong Lin, Shengyu Zhu, Lu Tan, Peng Cui:
ZIN: When and How to Learn Invariance Without Environment Partition? NeurIPS 2022 - [c129]Jiashuo Liu, Jiayun Wu, Bo Li, Peng Cui:
Distributionally Robust Optimization with Data Geometry. NeurIPS 2022 - [c128]Renzhe Xu, Xingxuan Zhang, Bo Li, Yafeng Zhang, Xiaolong Chen, Peng Cui:
Product Ranking for Revenue Maximization with Multiple Purchases. NeurIPS 2022 - [c127]Renzhe Xu, Xingxuan Zhang, Peng Cui, Bo Li, Zheyan Shen, Jiazheng Xu:
Regulatory Instruments for Fair Personalized Pricing. WWW 2022: 4-15 - [c126]Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui, Yong Jiang:
CausPref: Causal Preference Learning for Out-of-Distribution Recommendation. WWW 2022: 410-421 - [i56]Xiangmeng Wang, Qian Li, Dianer Yu, Peng Cui, Zhichao Wang, Guandong Xu:
Causal Disentanglement for Semantics-Aware Intent Learning in Recommendation. CoRR abs/2202.02576 (2022) - [i55]Yue He, Zimu Wang, Peng Cui, Hao Zou, Yafeng Zhang, Qiang Cui, Yong Jiang:
CausPref: Causal Preference Learning for Out-of-Distribution Recommendation. CoRR abs/2202.03984 (2022) - [i54]Renzhe Xu, Xingxuan Zhang, Peng Cui, Bo Li, Zheyan Shen, Jiazheng Xu:
Regulatory Instruments for Fair Personalized Pricing. CoRR abs/2202.04245 (2022) - [i53]Yong Lin, Shengyu Zhu, Peng Cui:
ZIN: When and How to Learn Invariance by Environment Inference? CoRR abs/2203.05818 (2022) - [i52]Xingxuan Zhang, Zekai Xu, Renzhe Xu, Jiashuo Liu, Peng Cui, Weitao Wan, Chong Sun, Chen Li:
Towards Domain Generalization in Object Detection. CoRR abs/2203.14387 (2022) - [i51]Xingxuan Zhang, Yue He, Renzhe Xu, Han Yu, Zheyan Shen, Peng Cui:
NICO++: Towards Better Benchmarking for Domain Generalization. CoRR abs/2204.08040 (2022) - [i50]Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao:
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection. CoRR abs/2205.10014 (2022) - [i49]Jiashuo Liu, Jiayun Wu, Jie Peng, Zheyan Shen, Bo Li, Peng Cui:
Distributionally Invariant Learning: Rationalization and Practical Algorithms. CoRR abs/2206.02990 (2022) - [i48]Renzhe Xu, Xingxuan Zhang, Bo Li, Yafeng Zhang, Xiaolong Chen, Peng Cui:
Product Ranking for Revenue Maximization with Multiple Purchases. CoRR abs/2210.08268 (2022) - [i47]Han Yu, Peng Cui, Yue He, Zheyan Shen, Yong Lin, Renzhe Xu, Xingxuan Zhang:
Stable Learning via Sparse Variable Independence. CoRR abs/2212.00992 (2022) - 2021
- [j41]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) - [j40]Yue He, Zheyan Shen, Peng Cui:
Towards Non-I.I.D. image classification: A dataset and baselines. Pattern Recognit. 110: 107383 (2021) - [j39]Yadan Luo, Zi Huang, Yang Li, Fumin Shen, Yang Yang, Peng Cui:
Collaborative Learning for Extremely Low Bit Asymmetric Hashing. IEEE Trans. Knowl. Data Eng. 33(12): 3675-3685 (2021) - [c125]Kai Wang, Zhene Zou, Qilin Deng, Jianrong Tao, Runze Wu, Changjie Fan, Liang Chen, Peng Cui:
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation. AAAI 2021: 4427-4435 - [c124]Jiashuo Liu, Zheyan Shen, Peng Cui, Linjun Zhou, Kun Kuang, Bo Li, Yishi Lin:
Stable Adversarial Learning under Distributional Shifts. AAAI 2021: 8662-8670 - [c123]Ke Tu, Peng Cui, Daixin Wang, Zhiqiang Zhang, Jun Zhou, Yuan Qi, Wenwu Zhu:
Conditional Graph Attention Networks for Distilling and Refining Knowledge Graphs in Recommendation. CIKM 2021: 1834-1843 - [c122]Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, Zheyan Shen:
Deep Stable Learning for Out-of-Distribution Generalization. CVPR 2021: 5372-5382 - [c121]Yijian Qin, Xin Wang, Peng Cui, Wenwu Zhu:
GQNAS: Graph Q Network for Neural Architecture Search. ICDM 2021: 1288-1293 - [c120]Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen:
Heterogeneous Risk Minimization. ICML 2021: 6804-6814 - [c119]Yue He, Peng Cui, Zheyan Shen, Renzhe Xu, Furui Liu, Yong Jiang:
DARING: Differentiable Causal Discovery with Residual Independence. KDD 2021: 596-605 - [c118]Haoxin Liu, Ziwei Zhang, Peng Cui, Yafeng Zhang, Qiang Cui, Jiashuo Liu, Wenwu Zhu:
Signed Graph Neural Network with Latent Groups. KDD 2021: 1066-1075 - [c117]Qilin Deng, Hao Li, Kai Wang, Zhipeng Hu, Runze Wu, Linxia Gong, Jianrong Tao, Changjie Fan, Peng Cui:
Globally Optimized Matchmaking in Online Games. KDD 2021: 2753-2763 - [c116]Yue He, Yancheng Dong, Peng Cui, Yuhang Jiao, Xiaowei Wang, Ji Liu, Philip S. Yu:
Purify and Generate: Learning Faithful Item-to-Item Graph from Noisy User-Item Interaction Behaviors. KDD 2021: 3002-3010 - [c115]Xin Wang, Peng Cui, Wenwu Zhu:
Out-of-distribution Generalization and Its Applications for Multimedia. ACM Multimedia 2021: 5681-5682 - [c114]Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, Zheyan Shen:
Kernelized Heterogeneous Risk Minimization. NeurIPS 2021: 21720-21731 - [c113]Daixin Wang, Zhiqiang Zhang, Jun Zhou, Peng Cui, Jingli Fang, Quanhui Jia, Yanming Fang, Yuan Qi:
Temporal-Aware Graph Neural Network for Credit Risk Prediction. SDM 2021: 702-710 - [c112]Meiqi Zhu, Xiao Wang, Chuan Shi, Houye Ji, Peng Cui:
Interpreting and Unifying Graph Neural Networks with An Optimization Framework. WWW 2021: 1215-1226 - [c111]Hande Dong, Jiawei Chen,