2024

Conference

ECOOP: Learning Gradual Typing Performance
Mohammad W. Khan, Sheng Chen, and Yi He
European Conference on Object-Oriented Programming

2023

Conference

AAAI: GLDL: Graph Label Distribution Learning
Yufei Jin, Richard Gao, Yi He , and Xingquan Zhu
the 38th AAAI Conference on Artificial Intelligence
[Code] [BibTeX]
AAAI: MKG-FENN: A Multimodal Knowledge Graph Fused End-to-end Neural Network for Accurate Drug–Drug Interaction Prediction
Di Wu, Wu Sun, Yi He ✉ , Zhong Chen, and Xin Luo
the 38th AAAI Conference on Artificial Intelligence
[Code] [BibTeX]
SDM: Robust Sparse Online Learning for Data Streams with Streaming Features
Zhong Chen, Yi He , Di Wu, Huixin Zhan, Victor S. Sheng, and Kun Zhang
SIAM International Conference on Data Mining
[BibTeX]
ICDM: IKGN: Intention-aware Knowledge Graph Network for POI Recommendation
Xiaoyu Zhu, Chenyang Bu, Bingbing Dong, Shengwei Ji, Yi He , and Xindong Wu
IEEE International Conference on Data Mining
[Code] [BibTeX]
ECML-PKDD: MMA: Multi-Metric-Autoencoder for Analyzing High-Dimensional and Incomplete Data
Cheng Lian, Di Wu, Yi He , Teng Huang, Zhong Chen, Xin Luo
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
[Code] [BibTeX]
IJCAI: Towards Utilitarian Online Learning -- A Review of Online Algorithms in Open Feature Space
Yi He , Christian Schreckenberger, Heiner Stuckenschmidt, Xindong Wu
The 32nd International Joint Conference on Artificial Intelligence
[BibTeX]

Journal

T.NNLS: Robust Low-Rank Latent Feature Analysis for Spatiotemporal Signal Recovery
Di Wu, Zechao Li, Zhikai Yu, Yi He , and Xin Luo
IEEE Transactions on Neural Networks and Learning Systems
[Paper] [BibTeX]
T.SC: MMLF: Multi-Metric Latent Feature Analysis for High-Dimensional and Incomplete Data
Di Wu, Peng Zhang, Yi He , and Xin Luo
IEEE Transactions on Services Computing
[Paper] [BibTeX]
T.KDE: Online Learning from Evolving Feature Spaces with Deep Variational Models
Heng Lian, Di Wu, Bo-Jian Hou, Jian Wu, and Yi He
IEEE Transactions on Knowledge and Data Engineering
[Paper] [BibTeX]
T.SC: A Lightweight Dynamic Storage Algorithm With Adaptive Encoding for Energy Internet
Song Deng, Yujia Zhai, Di Wu, Dong Yue, Xiong Fu, and Yi He
IEEE Transactions on Services Computing
[Paper] [BibTeX]

2022

Conference

AAAI: Online Semi-Supervised Learning with Mix-Typed Streaming Features
Di Wu, Shengda Zhuo, Yu Wang, Zhong Chen, and Yi He ✉
the 37th AAAI Conference on Artificial Intelligence
[Code] [BibTeX]
AAAI: Online Random Feature Forests for Learning in Varying Feature Spaces
Christian Schreckenberger, Yi He, Stefan Lüdtke, Christian Bartelt, and Heiner Stuckenschmidt
the 37th AAAI Conference on Artificial Intelligence
[BibTeX]
UIC: AutoRec++: Incorporating Debias Methods Into Autoencoder-Based Recommender System
Cheng Liang, Yi He, Teng Huang and Di Wu
the 19th IEEE International Conference on Ubiquitous Intelligence and Computing
[Code] [BibTeX]
UIC: OR-AutoRec: An Outlier-Resilient Autoencoder-Based Recommendation Model
Yuanpeng Hu, Xianmin Wang, Cheng Liang, Jing Li, Di Wu, and Yi He
the 19th IEEE International Conference on Ubiquitous Intelligence and Computing
[Code] [BibTeX]
ICNSC: Online Sparse Streaming Feature Selection with Uncertainty
Feilong Chen, Di Wu, Jie Yang, and Yi He
IEEE International Conference on Networking, Sensing and Control
[Paper] [Code] [BibTeX]
ACM MM: Online Deep Learning from Doubly-Streaming Data
Heng Lian, John Atwood, Bo-Jian Hou, Jian Wu, and Yi He ✉
ACM Multimedia (A*, rate = 690/2475=27.9%, ✉ Corresponding Author)
[Paper] [Code] [BibTeX]
ICMR: Camouflaged Poisoning Attack on Graph Neural Networks
Chao Jiang, Yi He ✉, Richard Chapman, and Hongyi Wu ✉
ACM International Conference on Multimedia Retrieval (rate = 54/157=34.4%, ✉ Corresponding Authors)
[Paper] [Code] [BibTeX]
EDGE: A Robust Latent Factor Analysis Model for Incomplete Data Recovery in Wireless Sensor Networks
Zhikai Yu, Di Wu, and Yi He *
IEEE International Conference on Edge Computing and Communications
[Paper] [BibTeX]
DASFAA: Toward Auto-Learning Hyperparameters for Deep Learning-Based Recommender Systems
Bo Sun, Di Wu, Mingsheng Shang, and Yi He
International Conference on Database Systems for Advanced Applications
[Paper] [Code] [BibTeX]

Journal

T.NNLS: A Prediction-Sampling-Based Multilayer-Structured Latent Factor Model for Accurate Representation to High-Dimensional and Sparse Data
Di Wu, Xin Luo, Yi He, and MengChu Zhou
IEEE Transactions on Neural Networks and Learning Systems
[Paper] [BibTeX]
T.SC: A Double-Space and Double-Norm Ensembled Latent Factor Model for Highly Accurate Web Service QoS Prediction
Di Wu, Peng Zhang, Yi He, and Xin Luo
IEEE Transactions on Services Computing
[Paper] [Code] [BibTeX]
T.II: A Quantitative Risk Assessment Model for Distribution Cyber Physical System under Cyber Attack
Song Deng, Jiangtang Zhang, Di Wu, Yi He, Xiangpeng Xie, and Xindong Wu
IEEE Transactions on Industrial Informatics
[Paper] [BibTeX]

2021

Conference

ICBK: Peng Zhang, Yi He, and Di Wu, "An Ensemble Latent Factor Model for Highly Accurate Web Service QoS Prediction", in 12th IEEE International Conference on Big Knowledge (ICBK), Auckland, New Zealand, December 7--8, 2021
ICDM: Yi He, Jiaxian Dong, Bo-Jian Hou, Yu Wang, and Fei Wang, "Online Learning in Variable Feature Spaces with Mixed Data", in IEEE International Conference on Data Mining (ICDM), Regular Paper, Auckland, New Zealand, December 7--10, 2021 (CORE Ranking = A*, Acceptance ratio: 98/990=9.9%)
KDD/w: Jing Chen, Yi He, and Vijay Raghavan, "Toward Wiser Funding Allocation with Bayesian Networks", in the 27th SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) Workshop -- Machine Learning for Consumers and Markets (MLCM), 2021
IJCAI: Yi He, Fudong Lin, Xu Yuan, and Nian-Feng Tzeng, "Interpretable Minority Synthesis for Imbalanced Classification", in the 30th International Joint Conference on Artificial Intelligence (IJCAI), Virtual Conference, 2021 (CORE Ranking = A*, Acceptance ratio: 587/4204=13.9%)
WWW: Yi He, Sheng Chen, Baijun Wu, Xu Yuan, and Xindong Wu, "Unsupervised Lifelong Learning with Curricula", in The Web Conference (WWW), Ljubljana, Slovenia, April 19--23, 2021 (CORE Ranking = A*, Acceptance ratio: 357/1736=20.6%)
AAAI: Yi He, Xu Yuan, Sheng Chen, and Xindong Wu, "Online Learning in Variable Feature Spaces under Incomplete Supervision", in AAAI Conference on Artificial Intelligence (AAAI), Virtual Conference, February 2--9, 2021 (CORE Ranking = A*, Acceptance ratio: 1692/9034=18.7%)

Journal

T.KDE: Zijie Pan, Li Hu, Weixuan Tang, Jin Li, Yi He, and Zheli Liu, "Privacy-Preserving Multi-Granular Federated Neural Architecture Search -- A General Framework", in IEEE Transactions on Knowledge and Data Engineering (TKDE), In Press (IF=6.98, JCR=Q2)
T.SMC: Di Wu, Yi He, and Xin Luo, and MengChu Zhou, "A Latent Factor Analysis-Based Approach to Online Sparse Streaming Feature Selection", in IEEE Transactions on Systems, Man, and Cybernetics: Systems (TSMC), In Press (IF=13.45, JCR=Q1)

2020 and Before

Conference

ICDM: Yi He, Xu Yuan, Nian-Feng Tzeng, and Xindong Wu, "Active Learning with Multi-Granular Graph Auto-Encoder", in IEEE International Conference on Data Mining (ICDM), Virtual Conference, November 17--20, 2020 (CORE Ranking = A*, Acceptance ratio: 183/930=19.7%)
IJCNN: Jing Chen, Yi He, and Vijay Raghavan, "Learning with Partial Multi-Outlooks," in IEEE International Joint Conference on Neural Networks (IJCNN), Virtual Conference, July 19--24, 2020 (Co-first Author, CORE Ranking = A)
ECAI: Yi He, Baijun Wu, Di Wu, and Xindong Wu, "On Partial Multi-Task Learning", in European Conference on Artificial Intelligence (ECAI), Virtual Conference, August 29--September 8, 2020 (CORE Ranking = A, Acceptance ratio: 365/1363=26.8%)
ECAI: Ege Beyazit, Yi He, Nian-Feng Tzeng, and Xindong Wu, "Online Learning to Accelerate Neural Network Inference with Traveling Classifiers", in European Conference on Artificial Intelligence (ECAI), Virtual Conference, August 29--September 8, 2020 (CORE Ranking = A, Acceptance ratio: 365/1363=26.8%)
SDM: Yi He, Sheng Chen, Thu Nguyen, Bruce A. Wade, and Xindong Wu, "Deep Matrix Tri-Factorization: Mining Vertex-wise Interactions in Multi-Space Attributed Graphs", in SIAM International Conference on Data Mining (SDM), Cincinnati, USA, May 7--9, 2020 (CORE Ranking = A, Acceptance ratio: 75/388=19.3%)
IJCAI: Yi He, Baijun Wu, Di Wu, Ege Beyazit, Sheng Chen, and Xindong Wu, "Online Learning from Capricious Data Streams: A Generative Approach", in International Joint Conference on Artificial Intelligence, Macau, China, August 10--16, 2019 (CORE Ranking = A*, Acceptance ratio: 650/4752=13.7%)
OOPSLA: Baijun Wu, John Campora, Yi He, Alexander Schlecht, and Sheng Chen, "Generating Precise Error Specifications for C: A Zero-Shot Learning Approach", in ACM Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA), Athens, Greece, October 20--25, 2019 (CORE Ranking = A*, Acceptance ratio: 72/201=35.8%)
BigData: Di Wu, Yi He, Xin Luo, Mingsheng Shang, and Xindong Wu, "Online Feature Selection with Capricious Streaming Features: A General Framework", in IEEE International Conference on Big Data (IEEE BigData), Los Angeles, USA, December 9--12, 2019 (CORE Ranking = B, Acceptance ratio: 18.7%)
PAKDD: Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and Xindong Wu, "A Data-Aware Latent Factor Model for Web Service QoS Prediction", in Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Macau, China, April 14--17, 2019 (CORE Ranking = A, Acceptance ratio: 140/567=24.7%)
ICTAI: Yi He, Di Wu, Ege Beyazit, Xiaoduan Sun, and Xindong Wu, "Supervised Data Synthesizing and Evolving -- A Framework for Real-World Traffic Crash Severity Classification", in IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Volos, Greece, November 5--7, 2018 (CORE Ranking = B)

Journal

T.NNLS: Yi He, Baijun Wu, Di Wu, Ege Beyazit, Sheng Chen, and Xindong Wu, "Toward Mining Capricious Data Streams: A Generative Approach", in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Volume 32, Issue 3, P1228-1240, 2020
T.KDE: Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and Xindong Wu, "A Data-Characteristic-Aware Latent Factor Model for Web Services QoS Prediction", in IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2020
T.SC: Di Wu, Qiang He, Xin Luo, Mingsheng Shang, Yi He, and Guoyin Wang, "A Posterior-neighborhood-regularized Latent Factor Model for Highly Accurate Web Service QoS Prediction", in IEEE Transactions on Services Computing, in press, 2019
T.SMC: Di Wu, Xin Luo, Mingsheng Shang, Yi He, Guoyin Wang, and Mengchu Zhou, "A Deep Latent Factor Model for High-Dimensional and Sparse Matrices in Recommender Systems", in IEEE Transactions on Systems, Man, and Cybernetics: Systems, in press, 2019
Applied Science: Dianlong You, Xindong Wu, Limin Shen, Yi He, Xu Yuan, Zhen Chen, Song Deng, and Chuan Ma, "Online Streaming Feature Selection via Conditional Independence", in Applied Science, vol.8, no.12, pp.2548--2572, 2019