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Works that I Personally Like Most...   

• Property-Aware Relation Networks for Few-Shot Molecular Property Prediction. TPAMI. 2024 (with my student)
• Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network.
  Nature Computational Science. 2023. (with my friend)
• Bilinear Scoring Function Search for Knowledge Graph Learning. TPAMI. 2023. (with my intern)
• Efficient Low-rank Tensor Learning with Nonconvex Regularization. JMLR. 2022. (1st author is me)
• Search to aggregate neighborhood for graph neural network. ICDE. 2021. (with my friend)
• Generalizing from a Few Examples: A Survey on Few-Shot Learning. CSUR. 2020. (with my intern)
• Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers. TPAMI. 2019. (1st author is me)
• Efficient Learning with Nonconvex Regularizers by Nonconvexity Redistribution. JMLR. 2018. (1st author is me)
• Co-teaching: Robust training deep neural networks with extremely noisy labels. NeurIPS. 2018. (with my friend)
• Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. KDD. 2017. (with my friend)

Journal / Conference

2024

1. Quanming Yao, Zhenqian Shen, Yaqing Wang, Dejing Dou. Property-Aware Relation Networks for Few-Shot Molecular Property Prediction. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). (paper, code)
2. Hansi Yang, Quanming Yao, Bo Han, James Kwok. Searching to Exploit Memorization Effect in Deep Learning with Noisy Labels. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). (paper, code)
3. Yaqing Wang, Zeifei Yang, Quanming Yao. Accurate and Interpretable Drug-drug Interaction Prediction Enabled by Knowledge Subgraph Learning. Communications Medicine (Nature Series). (paper, code), (News from Tsinghua University)
4. Hansi Yang, Quanming Yao. Topology-aware Tensor Decomposition for Meta-graph Learning. CAAI Transactions on Intelligence Technology (CAAI TRIT). (paper)
5. Shiguang Wu, Yaqing Wang, Quanming Yao. PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction. International Joint Conference on Artificial Intelligence (IJCAI). (paper)
6. Haiquan Qiu, Yongqi Zhang, Yong Li, Quanming Yao. Understanding Expressivity of GNN in Rule Learning. International Conference on Learning Representations (ICLR). (paper, code)
7. Zhanke Zhou, Yongqi Zhang, Jiangchao Yao, Quanming Yao, Bo Han. Less is More: One-shot Subgraph Reasoning on Large-scale Knowledge Graphs. International Conference on Learning Representations (ICLR). (paper, code)
8. Quanming Yao. Towards Human-like Learning from Relational Structured Data. AAAI Conference on Artificial Intelligence (AAAI). (paper)
9. Lebin Yu, Yunbo Qiu, Quanming Yao, Yuan Shen, Xudong Zhang, Jian Wang. Robust Communicative Multi-Agent Reinforcement Learning with Active Defense. AAAI Conference on Artificial Intelligence (AAAI). (paper)
10. Guangyi Liu, Quanming Yao, Yongqi Zhang, Lei Chen. Knowledge-Enhanced Recommendation with User-Centric Subgraph Network. IEEE International Conference on Data Engineering (ICDE). (paper, code)
11. Shuzhi Liu, Shimin Di, Jianwen Peng, Quanming Yao. Path-based Link Prediction on Hyper-relational Knowledge Graph. IEEE Conference on Artificial Intelligence (IEEE CAI). (paper)
12. Shiqi Fan, Hongyi Nie, Ruibing Wang, Quanming Yao, Haotong Du, Yang Liu, Zhen Wang. Relationship-Entity Hybrid Learning Graph Model for Few-Shot Temporal Knowledge Graph Forecasting. International Conference on Database Systems for Advanced Applications (DASFAA). (paper)

2023

1. Yongqi Zhang, Quanming Yao, Ling Yue, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng. Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network. Nature Computational Science. (arxiv, publisher, code), (News from Tsinghua University, High-lights from NSFC)
2. Yongqi Zhang, Quanming Yao, James T. Kwok. Bilinear Scoring Function Search for Knowledge Graph Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). (paper, code)
3. Hui Zhang, Quanming Yao, James T. Kwok, Xiang Bai. Searching a High Performance Feature Extractor for Text Recognition Network. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). (paper, code)
4. Lanning Wei, Huan Zhao, Zhiqiang He, Quanming Yao. Neural Architecture Search for GNN-based Graph Classification. Transactions on Information Systems (TOIS). (paper, code)
5. Zhen Xu, Quanming Yao, Yong Li, Qiang Yang. Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks. Transaction of Machine Learning Research (TMLR) / AutoML Conference (Journal Track). (paper)
6. Hengjin Ke, Dan Chen, Quanming Yao, Yunbo Tang, Jia Wu, Jessica Monaghan, Paul Sowman, David McAlpine. Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). (paper)
7. Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng. Relation-aware Ensemble Learning for Knowledge Graph Embedding. Conference on Empirical Methods in Natural Language Processing (EMNLP). (paper, code)
8. Zhenqian Shen, Hansi Yang, Yong Li, James Kwok, Quanming Yao. Efficient Hyper-parameter Optimization with Cubic Regularization. Annual Conference on Neural Information Processing Systems (NeurIPS). (paper)
9. Zhanke Zhou, Jiangchao Yao, Jiaxu Liu, Xiawei Guo, Quanming Yao, Li He, Liang Wang, Bo Zheng, Bo Han. Combating Bilateral Edge Noise for Robust Link Prediction. Annual Conference on Neural Information Processing Systems (NeurIPS). (paper)
10. Hansi Yang, Yongqi Zhang, Quanming Yao, James T. Kwok. Positive-Unlabeled Node Classification with Structure-aware Graph Learning. ACM International Conference on Information and Knowledge Management (CIKM). (paper)
11. Yongqi Zhang, Zhanke Zhou, Quanming Yao, Xiaowen Chu, Bo Han. Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). (paper, code)
12. Xu Wang, Huan Zhao, Wei-Wei Tu, Quanming Yao. Automated 3D Pre-Training for Molecular Property Prediction. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD). (paper, code)
13. Zhanke Zhou, Chenyu Zhou, Xuan Li, Jiangchao Yao, Quanming Yao, Bo Han. On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation. International Conference on Machine Learning (ICML). (paper)
14. Hongzhi Shi, Jingtao Ding, Yufan Cao, Quanming Yao, Li Liu, Yong Li. Learning Symbolic Models for Graph-structured Physical Mechanism. International Conference on Learning Representations (ICLR). (paper)
15. Jianing Zhu, Jiangchao Yao, Tongliang Liu, Quanming Yao, Jianliang Xu, Bo Han. Combating Exacerbated Heterogeneity for Robust Decentralized Models. International Conference on Learning Representations (ICLR). (paper)
16. Shiguang Wu, Yaqing Wang, Qinghe Jing, Daxiang Dong, Dejing Dou, Quanming Yao. ColdNAS: Search to Modulate for User Cold-Start Recommendation. The Web Conference (WebConf). (paper, code)
17. Lanning Wei, Zhiqiang He, Huan Zhao, Quanming Yao. Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification. The Web Conference (WebConf). (paper, code)
18. Hongzhi Shi, Quanming Yao, Yong Li. Learning to Simulate Crowd Trajectories with Graph Networks. The Web Conference (WebConf). (paper)
19. Lebin Yu, Yunbo Qiu, Quanming Yao, Xudong Zhang and Jian Wang. Improving Zero-Shot Coordination Performance Based on Policy Similarity. International Conference on Automated Planning and Scheduling (ICAPS). (paper)
20. Yongqi Zhang, Hui Zhang, Quanming Yao, Jun Wan. Combining Self-Supervised and Supervised Learning with Noisy Labels. IEEE International Conference on Image Processing (ICIP). (paper)
21. Hansi Yang, Peiyu Zhang, Quanming Yao. Tensorizing Subgraph Search in the Supernet. Asian Conference on Artificial Intelligence Technology (ACAIT). (best student paper)

2022

1. Wei He, Quanming Yao, Chao Li, Naoto Yokoya, Qibin Zhao, Hongyan Zhang, Liangpei Zhang. Non-local Meets Global: An Integrated Paradigm for Hyperspectral Restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). (paper, code)
2. Quanming Yao, Hansi Yang, En-Liang Hu, James T. Kwok. Efficient Low-Rank Semidefinite Programming with Robust Loss Functions. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). (paper [IEEE link], [arxiv link])
3. Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok. Efficient Low-rank Tensor Learning with Nonconvex Regularization. Journal of Machine Learning Research (JMLR). (paper, code)
4. Zhen Xu, Sergio Escalera, Adrien Pavao, Magali Richard, Weiwei Tu, Quanming Yao, Huan Zhao, Isabelle Guyon. Codabench: Flexible, Easy-to-use and Reproducible Meta-benchmark Platform. Patterns (Cell Press). (paper)
5. Huiming Chen, Huandong Wang, Quanming Yao, Yong Li, Depeng Jin, Qiang Yang. LoSAC: An Efficient Local Stochastic Average Control Method for Federated Optimization. ACM Transactions on Knowledge Discovery from Data (TKDD). (paper)
6. Haotong Du, Zhen Wang, Hongyi Nie, Quanming Yao, Xuelong Li. Multi-scale Dilated Convolutional Network for Knowledge Graph Embedding. SCIENTIA SINICA: Informationis (in Chinese). (paper)
7. Yang Liu, Xinle Liang, Jiahuan Luo, Yuanqin He, Tianjian Chen, Quanming Yao, Qiang Yang. Cross-Silo Federated Neural Architecture Search for Heterogeneous and Cooperative Systems. Federated and Transfer Learning (book chapter). (paper)
8. Kaixin Zheng, Yaqing Wang, Quanming Yao and Dejing Dou. Simplified Graph Learning for Inductive Short Text Classification. Empirical Methods in Natural Language Processing (EMNLP). (paper)
9. Zhen Wang, Haotong Du, Quanming Yao and Xuelong Li. Search to Pass Messages for Temporal Knowledge Graph Completion. Empirical Methods in Natural Language Processing (EMNLP). (paper)
10. Wei He, Quanming Yao, Naoto Yokoya, Tatsumi Uezato, Hongyan Zhang and Liangpei Zhang. Spectrum-aware and Transferable Architecture Search for Hyperspectral Image Restoration. European Conference on Computer Vision (ECCV). (paper)
11. Xu Wang, Huan Zhao, Lanning Wei and Quanming Yao. Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search. International Workshop on Deep Learning on Graphs: Methods and Applications (DLG-KDD). (paper, code)
12. Zhen Xu, Lanning Wei, Huan Zhao, Rex Ying, Quanming Yao, Weiwei Tu, Isabelle Guyon. Bridging the Gap of AutoGraph between Academia and Industry: Analysing AutoGraph Challenge at KDD Cup 2020. Frontiers in Artificial Intelligence. (paper, code)
13. Haiquan Qiu, Yao Wang, Shaojie Tang, Deyu Meng, Quanming Yao. Fast and Provable Nonconvex Tensor RPCA. International Conference on Machine Learning (ICML). (paper)
14. Yongqi Zhang, Zhanke Zhou, Quanming Yao, Yong Li. KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning. Annual Meeting of the Association for Computational Linguistics (ACL). (paper, code)
15. Yongqi Zhang, Quanming Yao. Knowledge Graph Reasoning with Relational Digraph. The Web Conference (WebConf). (paper, code)
16. Yinfeng Li, Chen Gao, Quanming Yao, Tong Li, Depeng Jin, Yong Li. DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Prediction. International Conference on Data Engineering (ICDE). (paper)

2021

1. Hugo Jair Escalante, Quanming Yao, Weiwei Tu, Nelishia Pillay, Rong Qu, Yang Yu, Neil Houlsby. Automated Machine Learning (Guest Editorial). IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). (paper [IEEE link])
2. Huan Zhao, Quanming Yao, Yangqiu Song, James T. Kwok, Dik Lee. Side Information Fusion for Recommender Systems over Heterogeneous Information Network. ACM Transactions on Knowledge Discovery from Data (TKDD). (paper; code)
3. Yongqi Zhang, Quanming Yao, Lei Chen. Simple and Automated Negative Sampling for Knowledge Graph Embedding. The International Journal on Very Large Data Bases (VLDBJ). (paper, code)
4. Yunbo Tang, Dan Chen, Yiping Zuo, Xiaoqiang Lu, Rajiv Ranjan, Albert Zomaya, Quanming Yao, Xiaoli Li. Enhanced Bayesian Factorization with Variant Scale Partitioning for Multivariate Time Series Analysis. IEEE Transactions on Knowledge and Data Engineering (TKDE). (paper)
5. Wei Zheng, Zhen Wang, Quanming Yao, Xuelong Li. WRTRe: Weighted Relative Position Transformer for Joint Entity and Relation Extraction. Neurocomputing.
6. Chen Gao, Yinfeng Li, Quanming Yao, Depeng Jin, Yong Li. Progressive Feature Interaction Search for Deep Sparse Network. Annual Conference on Neural Information Processing Systems (NeurIPS). (paper, code)
7. Fengli Xu, Quanming Yao, Pan Hui, Yong Li. Automorphic Equivalence-aware Graph Neural Network. Annual Conference on Neural Information Processing Systems (NeurIPS). (paper, code)
8. Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, Dejing Dou. Property-Aware Relation Networks for Few-Shot Molecular Property Prediction. Annual Conference on Neural Information Processing Systems (NeurIPS). (paper, code)
9. Yaqing Wang, Song Wang, Quanming Yao, Dejing Dou. Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification. Conference on Empirical Methods in Natural Language Processing (EMNLP). (paper, code)
10. Lanning Wei, Huan Zhao, Quanming Yao, Zhiqiang He. Pooling Architecture Search for Graph Classification. ACM International Conference on Information and Knowledge Management (CIKM). (paper, code)
11. Yuhui Ding, Quanming Yao, Huan Zhao, Tong Zhang. DiffMG: Differentiable Meta Graph Search for Heterogeneous Graph Neural Networks. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). (paper, code)
12. Chen Gao, Quanming Yao, Yong Li, Depeng Jin. Efficient Data-specific Model Search for Collaborative Filtering. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). (paper)
13. Yaqing Wang, Quanming Yao, James T. Kwok. A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning. The Web Conference (WebConf). (paper)
14. Shimin Di, Quanming Yao, Lei Chen. Searching to Sparsify Tensor Decomposition for N-ary Relational Data. The Web Conference (WebConf). (paper, code)
15. Yu Liu, Quanming Yao, Yong Li. Role-Aware Modeling for N-ary Relational Knowledge Bases. The Web Conference (WebConf). (paper, code)
16. Shimin Di, Quanming Yao, Yongqi Zhang, Lei Chen. Efficient Relation-aware Scoring Function Search for Knowledge Graph Embedding. International Conference on Data Engineering (ICDE). (paper, code)
17. Huan Zhao, Quanming Yao, Weiwei Tu. Search to aggregate neighborhood for graph neural network. International Conference on Data Engineering (ICDE). (paper, code)

2020

1. Yaqing Wang, Quanming Yao, James T. Kwok, Lionel Ni. Generalizing from a Few Examples: A Survey on Few-Shot Learning. ACM Computing Surveys (CSUR). (paper, update-to-date reference list)
2. Yongqi Zhang, Quanming Yao, Lei Chen. Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding. Annual Conference on Neural Information Processing Systems (NeurIPS). (paper, code)
3. Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, Depeng Jin. Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering. Annual Conference on Neural Information Processing Systems (NeurIPS). (paper, code)
4. Hui Zhang, Quanming Yao, Mingkun Yang, Yongchao Xu, Xiang Bai. AutoSTR: Efficient Backbone Search for Scene Text Recognition. European Conference on Computer Vision (ECCV). (paper, code)
5. Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James T. Kwok. Searching to Exploit Memorization Effect in Learning from Noisy Labels. International Conference on Machine Learning (ICML). (paper, code)
6. Bo Han, Gang Niu, Xingrui Yu, Quanming Yao, Miao Xu, Ivor Tsang, Masashi Sugiyama. SIGUA: Forgetting May Make Learning with Noisy Labels More Robust. International Conference on Machine Learning (ICML). (paper)
7. Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li, Cho-Jui Hsieh. Efficient Neural Interaction Functions Search for Collaborative Filtering. The Web Conference (WebConf). (paper, code)
8. Yu Liu, Quanming Yao, Yong Li. Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases. The Web Conference (WebConf). (paper, code)
9. Quanming Yao, Ju Xu, Weiwei Tu, Zhanxing Zhu. Efficient Neural Architecture Search via Proximal Iterations. AAAI Conference on Artificial Intelligence (AAAI). (paper, code)
10. Yongqi Zhang, Quanming Yao, Wenyuan Dai, Lei Chen. AutoSF: Searching Scoring Functions for Knowledge Graph Embedding. IEEE International Conference on Data Engineering (ICDE). (paper, code)
11. Hongzhi Shi, Quanming Yao, Qi Guo, Yaguang Li, Lingyu Zhang, Jieping Ye, Yong Li, Yan Liu. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network. IEEE International Conference on Data Engineering (ICDE). (paper)

2019

1. Quanming Yao, James T. Kwok, Taifeng Wang, Tie-Yan Liu. Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). (paper; Matlab code, C++ code)
2. Quanming Yao, James T. Kwok. Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion. IEEE Transactions on Knowledge and Data Engineering (TKDE). (paper; code for matrix completion, code for tensor completion)
3. En-liang Hu, Quanming Yao. Robust Learning from Noisy Side-information by Semi-definite Programming. International Joint Conference on Artificial Intelligence (IJCAI). (paper)
4. Quanming Yao, Xiawei Guo, James T. Kwok, Weiwei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang. Differential Private Stack Generalization with an Application to Diabetes Prediction. International Joint Conference on Artificial Intelligence (IJCAI). (paper, slides)
5. Hongzhi Shi, Chao Zhang, Quanming Yao, Yong Li, Funing Sun, Depeng Jin. State-Sharing Sparse Hidden Markov Models for Personalized Sequences. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). (paper, slides; code)
6. Yuanfei Luo, Mengshuo Wang, Hao Zhou, Quanming Yao, Weiwei Tu, Yuqiang Chen, Qiang Yang, Wenyuan Dai. AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). (paper, benchmark data, 3 mins video)
7. Quanming Yao, James T. Kwok, Bo Han. Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. International Conference on Machine Learning (ICML). (paper, code, slides and 5 mins video)
8. Wei He, Quanming Yao, Chao Li, Naoto Yokoya, Qibin Zhao. Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising. IEEE Conference on Computer Vision and Pattern Recognition (CVPR, oral). (paper, code)
9. Yongqi Zhang, Quanming Yao, Yingxia Shao, Lei Chen. NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding. IEEE International Conference on Data Engineering (ICDE). (paper; code)

2018

1. Quanming Yao, James T. Kwok. Efficient Learning with Nonconvex Regularizers by Nonconvexity Redistribution. Journal of Machine Learning Research (JMLR). (paper; code)
2. Yaqing Wang, Quanming Yao, James T. Kwok, Lionel Ni. Scalable Online Convolutional Sparse Coding. IEEE Transactions on Image Processing (TIP). (paper; code)
3. Bo Han, Quanming Yao, Yuangang Pan, Ivor Tsang, Xiaokui Xiao, Qiang Yang, Masashi Sugiyama. Millionaire: A Hint-guided Approach for Crowdsourcing. Machine Learning (MLJ). (paper)
4. Quanming Yao, James T. Kwok. Scalable Robust Matrix Factorization with Nonconvex Loss. Advance in Neural Information Processing Systems (NeurIPS). (paper)
5. Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, Masashi Sugiyama. Co-teaching: Robust training deep neural networks with extremely noisy labels. Advance in Neural Information Processing Systems (NeurIPS). (paper, slides; code)
6. Yaqing Wang, Quanming Yao, James T. Kwok, Lionel Ni. Online Convolutional Sparse Coding with Sample-Dependent Dictionary. International Conference on Machine Learning (ICML). (paper; code)

2017

1. Yi Yang, Quanming Yao, Huamin Qu. VISTopic: A Visual Analytics System for Making Sense of Large Document Collections using Hierarchical Topic Modeling. Journal of Visual Informatics. (paper)
2. Huan Zhao, Quanming Yao, James T. Kwok, Dik Lee. Collaborative Filtering with Social Local Models. IEEE International Conference on Data Mining (ICDM). (paper)
3. Huan Zhao, Quanming Yao, Jianda Li, Yangqiu Song, Dik Lee. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). (paper; code)
   --- 12/220 in KDD 2017, 34/1391 in KDD 2016-2020
4. Quanming Yao, James T. Kwok. Fei Gao, Wei Chen, Tie-Yan Liu. Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems. International Joint Conference on Artificial Intelligence (IJCAI). (paper; code)
5. Lu Hou, Quanming Yao, James T. Kwok. Loss-aware Binarization of Deep Networks. International Conference on Learning Representations (ICLR). (paper; code)
6. Yaqing Wang, James T. Kwok, Quanming Yao, Lionel Ni. Zero-Shot Learning with a Partial Set of Observed Attributes. International Joint Conference on Neural Networks (IJCNN). (paper)
7. Xiawei Guo, Quanming Yao, James T. Kwok. Efficient Sparse Low-Rank Tensor Completion using Frank-Wolfe Algorithm. AAAI Conference on Artificial Intelligence (AAAI). (paper, code)

2016

1. Quanming Yao, James T. Kwok. Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. International Conference on Machine Learning (ICML). (paper; code)
2. Quanming Yao, James T. Kwok. Greedy Learning of Generalized Low-Rank Models. International Joint Conference on Artificial Intelligence (IJCAI). (paper)

2015

1. Quanming Yao, James T. Kwok, Wenliang Zhong. Fast Low-Rank Matrix Learning with Nonconvex Regularization. IEEE International Conference on Data Mining (ICDM). (paper, code)
2. Quanming Yao, James T. Kwok. Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion. International Joint Conference on Artificial Intelligence (IJCAI). (paper; code)
3. Quanming Yao, James T. Kwok. Colorization by Patch-Based Local Low-Rank Matrix Completion. AAAI Conference on Artificial Intelligence (AAAI). (paper)

Book

2020

1. Xiawei Guo, Quanming Yao, James T. Kwok, Weiwei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang. Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. Federated Learning 2020 (book chapter). (book)