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Pre-prints   

  1. Y. Zhang, Q. Yao, L. Chen. Neural Recurrent Structure Search for Knowledge Graph Embedding. Arvix. Nov. 2019. (paper)
  2. H. Yang, Q. Yao, B. Han, G. Niu. Searching to Exploit Memorization Effect in Learning from Corrupted Labels. Arvix. Nov. 2019. (paper)
  3. Y. Wang, Q. Yao, J. Kwok, L. Ni. Generalizing from a Few Examples: A Survey on Few-Shot Learning. ACM CSUR (major revision). Sep. 2019. (the draft is kept updating; any comments and suggestions are welcome)
  4. W. He, Q. Yao, L. Chao, N. Yokoya, Q. Zhao, H. Zhang, L. Zhang Non-local Meets Global: An Integrated Paradigm for Hyperspectral Restoration. Submitted to TPAMI. Sep. 2019.
  5. H. Zhao, Q. Yao, Y. Song, J. Kwok, D. Lee. Learning with Heterogeneous Side Information Fusion for Recommender Systems. Submitted to TKDD. Sep. 2019. (paper, invited talk@ACML-2018, slides, code)
  6. Y. Zhang, Q. Yao, L. Chen. Simple and Automated Negative Sampling for Knowledge Graph Embedding. Submitted to TKDE. May. 2019.
  7. Y. Zhang, Q. Yao, W. Dai, L. Chen. AutoKGE: Searching Scoring Functions for Knowledge Graph Embedding. Arvix. Apr. 2019. (paper, code)

Publications

2020
  1. Q. Yao, X. Chen, J. Kwok, Y. Li, C.-J. Hsieh. Efficient Neural Interaction Functions Search for Collaborative Filtering. The Web Conference (WWW). 2020. (paper, code)
  2. Y. Liu, Q. Yao, Y. Li. Generalizing Tensor Decomposition for N-ary Relational Knowledge Bases. The Web Conference (WWW). 2020. (paper, code)
  3. Q. Yao, J. Xu, W. Tu, Z. Zhu. Efficient Neural Architecture Search via Proximal Iterations. AAAI Conference on Artificial Intelligence (AAAI). 2020. (paper, code)
  4. H. Shi, Q. Yao, Q. Guo, Y. Li, L. Zhang, J. Ye, Y. Li, Y. Liu. Predicting Origin-Destination Flow via Multi-Perspective Graph Convolutional Network. IEEE International Conference on Data Engineering (ICDE). 2020. (paper)
2019
  1. H. Yang, Q. Yao. Towards Automated Learning from Noisy Labels. Asian Conference on Machine Learning: Weakly-supervised Learning Workshop (WSL@ACML). 2019. (best paper award)
  2. E. Hu, Q. Yao. Robust Learning from Noisy Side-information by Semi-definite Programming. International Joint Conference on Artificial Intelligence (IJCAI). 2019. (paper)
  3. Q. Yao, X. Guo, J. Kwok, W. Tu, Y. Chen, D. Wen, Q. Yang. Differential Private Stack Generalization with an Application to Diabetes Prediction. International Joint Conference on Artificial Intelligence (IJCAI). 2019. (paper, slides)
  4. H. Shi, C. Zhang, Q. Yao, Y. Li, F. Sun, D. Jin. State-Sharing Sparse Hidden Markov Models for Personalized Sequences. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 2019. (paper, slides)
  5. Y. Luo, M. Wang, H. Zhou, Q. Yao, W. Tu, Y. Chen, Q. Yang, W. Dai. AutoCross: Automatic Feature Crossing for Tabular Data in Real-World Applications. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). 2019. (paper, benchmark data, 3 mins video)
  6. Q. Yao, J. Kwok, B. Han. Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. International Conference on Machine Learning (ICML). 2019. (paper, appendix, code, slides and 5 mins video)
  7. W. He, Q. Yao, C. Li, N. Yokoya, Q. Zhao. Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising. IEEE Conference on Computer Vision and Pattern Recognition (CVPR, oral). 2019. (paper, code)
  8. Y. Zhang, Q. Yao, Y. Shao, L. Chen. NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding. IEEE International Conference on Data Engineering (ICDE). 2019. (paper, slides; code)
2018
  1. Q. Yao, J. Kwok. Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion. IEEE Transactions on Knowledge and Data Engineering (TKDE). 2018. (paper; code for matrix completion, code for tensor completion)
  2. B. Han, Q. Yao, Y. Pan, I. Tsang, X. Xiao, Q. Yang, M. Sugiyama. Millionaire: A Hint-guided Approach for Crowdsourcing. Machine Learning (MLJ). 2018. (paper)
  3. Q. Yao, J. Kwok, T. Wang, T. Liu. Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). 2018. (paper; Matlab code, C++ code)
  4. Y. Wang, Q. Yao, J. Kwok, L. Ni. Scalable Online Convolutional Sparse Coding. IEEE Transactions on Image Processing (TIP), 2018. (paper; code)
  5. Q. Yao, J. Kwok. Efficient Learning with Nonconvex Regularizers by Nonconvexity Redistribution. Journal of Machine Learning Research (JMLR), 2018. (paper; code)
  6. Q. Yao, J. Kwok. Scalable Robust Matrix Factorization with Nonconvex Loss. Advance in Neural Information Processing Systems (NeurIPS). 2018. (paper)
  7. B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, M. Sugiyama. Co-teaching: Robust training deep neural networks with extremely noisy labels. Advance in Neural Information Processing Systems (NeurIPS). 2018. (paper, slides; code)
  8. Y. Wang, Q. Yao, J. Kwok, L. Ni. Online Convolutional Sparse Coding with Sample-Dependent Dictionary. International Conference on Machine Learning (ICML), 2018. (paper; code)
2017
  1. H. Zhao, Q. Yao, J. Kwok, D. Lee. Collaborative Filtering with Social Local Models. IEEE International Conference on Data Mining (ICDM), 2017. (paper)
  2. H. Zhao, Q. Yao, J. Li, Y. Song, D. Lee. Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks. ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017. (paper; code)
  3. Q. Yao, J. Kwok. F. Gao, W. Chen, T. Liu. Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems. International Joint Conference on Artificial Intelligence (IJCAI), 2017. (paper; code)
  4. L. Hou, Q. Yao, J. Kwok. Loss-aware Binarization of Deep Networks. International Conference on Learning Representations (ICLR), 2017. (paper; code)
  5. Y. Wang, J. Kwok, Q. Yao, L. Ni. Zero-Shot Learning with a Partial Set of Observed Attributes. International Joint Conference on Neural Networks (IJCNN), 2017. (paper)
  6. X. Guo, Q. Yao, J. Kwok. Efficient Sparse Low-Rank Tensor Completion using Frank-Wolfe Algorithm. AAAI Conference on Artificial Intelligence (AAAI), 2017. (paper, code)
  7. Y. Yang, Q. Yao, H. Qu. VISTopic: A Visual Analytics System for Making Sense of Large Document Collections using Hierarchical Topic Modeling. Journal of Visual Informatics, 2017. (paper)
2016
  1. Q. Yao, J. Kwok. Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. International Conference on Machine Learning (ICML), 2016. (paper; code)
  2. Q. Yao, J. Kwok. Greedy Learning of Generalized Low-Rank Models. International Joint Conference on Artificial Intelligence (IJCAI), 2016. (paper, appendix, slide)
2015
  1. Q. Yao, J. Kwok, W. Zhong. Fast Low-Rank Matrix Learning with Nonconvex Regularization. IEEE International Conference on Data Mining (ICDM), 2015. (paper, slide; code)
  2. Q. Yao, J. Kwok. Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion. International Joint Conference on Artificial Intelligence (IJCAI), 2015. (paper, slide, poster; code)
  3. Q. Yao, J. Kwok. Colorization by Patch-Based Local Low-Rank Matrix Completion. AAAI Conference on Artificial Intelligence (AAAI), 2015. (paper, slide)
Earlier
  1. Q. Yao, X. Jiang, M. Gong, X. You, Y. Liu, D. Xu. Efficient Group Learning with Hypergraph Partition in Multi-task Learning. Chinese Conference on Pattern Recognition (CCPR), 2012. (paper)

Workshop Papers  

  1. Q. Yao, M. Wang, Y. Li, W. Tu, Q. Yang, Y. Yu. Taking Human out of Learning Applications: A Survey on Automated Machine Learning. Arvix. Nov. 2018. (the draft is kept updating; any comments and suggestions are welcome)
  2. Z. Liu, O. Bousquet A. Elisseeff, S. Escalera, I. Guyon, J. Jacques Jr., A. Pavao, D. Silver, L. Sun, S. Treguer, W. Tu, J. Wang, Q. Yao. AutoDL Challenge Design and Beta Tests - Towards automatic deep learning. Meta-learning workshop@NeurIPS-2018. 2018. (paper)
  3. Q. Yao, J. Kwok, X. Guo. Fast Learning with Nonconvex L1-2 Regularization. ArXiv. Oct. 2016. (paper)