| Joint ranking SVM and binary relevance with robust low-rank learning for multi-label classification G Wu, R Zheng, Y Tian, D Liu Neural Networks 122, 24-39, 2020 | 85 | 2020 |
| Cost-sensitive multi-label learning with positive and negative label pairwise correlations G Wu, Y Tian, D Liu Neural Networks 108, 411-423, 2018 | 42 | 2018 |
| Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? G Wu, J Zhu NeurIPS (arXiv preprint arXiv:2011.07805), 2020 | 29 | 2020 |
| Stability and generalization of bilevel programming in hyperparameter optimization F Bao, G Wu, C Li, J Zhu, B Zhang Advances in neural information processing systems 34, 4529-4541, 2021 | 23 | 2021 |
| A unified framework implementing linear binary relevance for multi-label learning G Wu, Y Tian, C Zhang Neurocomputing 289, 86-100, 2018 | 18 | 2018 |
| Revisiting Discriminative vs. Generative Classifiers: Theory and Implications C Zheng, G Wu, F Bao, Y Cao, C Li, J Zhu ICML 2023 (arXiv preprint arXiv:2302.02334), 2023 | 16 | 2023 |
| Toward Understanding Generative Data Augmentation C Zheng, G Wu, C Li NeurIPS 2023 (arXiv preprint arXiv:2305.17476), 2023 | 9 | 2023 |
| On the convergence of prior-guided zeroth-order optimization algorithms S Cheng, G Wu, J Zhu Advances in Neural Information Processing Systems 34, 14620-14631, 2021 | 9 | 2021 |
| Rethinking and reweighting the univariate losses for multi-label ranking: Consistency and generalization G Wu, C Li, K Xu, J Zhu Advances in Neural Information Processing Systems 34, 14332-14344, 2021 | 6 | 2021 |
| Stochastic gradient descent for large-scale linear nonparallel svm J Tang, Y Tian, G Wu, D Li Proceedings of the International Conference on Web Intelligence, 980-983, 2017 | 5 | 2017 |
| Privileged Multi-Target Support Vector Regression G Wu, Y Tian, D Liu 2018 24th International Conference on Pattern Recognition (ICPR), 385-390, 2018 | 4 | 2018 |
| Exploration in mapping kernel-based home range models from remote sensing imagery with conditional adversarial networks R Zheng, G Wu, C Yan, R Zhang, Z Luo, B Yan Remote Sensing 10 (11), 1722, 2018 | 3 | 2018 |
| Towards Understanding Generalization of Macro-AUC in Multi-label Learning G Wu, C Li, Y Yin ICML 2023 (arXiv preprint arXiv:2305.05248), 2023 | 1 | 2023 |
| Deep ensemble as a Gaussian process approximate posterior Z Deng, F Zhou, J Chen, G Wu, J Zhu arXiv preprint arXiv:2205.00163, 2022 | 1 | 2022 |
| Can Infinitely Wide Deep Nets Help Small-data Multi-label Learning? G Wu, J Zhu Asian Conference on Machine Learning, 1494-1509, 2024 | | 2024 |
| DiffAIL: Diffusion Adversarial Imitation Learning B Wang, Y Zhang, T Pang, G Wu, Y Yin AAAI 2024 (arXiv preprint arXiv:2312.06348), 2023 | | 2023 |
| Rehearmixup: Improving Rehearsal-Based Continual Learning Y Zhang, G Wu, Y Yin Available at SSRN 4485798, 0 | | |
Ying-Jie TianChinese Academy of SciencesZweryfikowany adres z gucas.ac.cn
