如何看待重庆大学引进 25 岁博导冯磊，入职半年实现学院 ICML 顶会论文零的突破？
但就26岁（入职时25）的年纪来看，冯磊的成就就已经相当辉煌了。看他过往的成绩，至少他本人是相当“有料”的，当然。这并不意味着他也会是一名很好的博导，这一点还需要时间去证明。 横向来看，近来很多学霸当网红的消息，虽然无可厚非，但冯磊的出现，却给中国各大高校的学霸学神们立了一个新榜样。北大"韦神”和冯磊这样的青年才俊越多，中国突破“高精尖”的希望才越大。这样的现象当然是越多越好。  一波不重要的资料： 冯磊入职时仅25岁，是重大计算机学院目前年龄最小的引进人才，也是该学院有史以来首次直接给应届博士毕业生正高/博导岗位。此外，冯磊还入选了2021福布斯中国30 Under 30 科学和医疗健康领域榜单。 个人主页：http://www.cs.cqu.edu.cn/info/1325/5242.htm 入职半年，冯磊撰写的论文《Pointwise Binary Classification with Pairwise Confidence Comparisons》在第38届国际机器学习会议（The 38th International Conference on Machine Learning）（CCF A类）上发表。这是机器学习领域公认的顶级国际学术会议，在学术界享有极高的声誉，这也是重庆大学计算机学院首次以第一单位在该会议上发表学术论文，实现了零的突破。 代表性论文 [20] Tao Liang, Guosheng Lin, Lei Feng, Yan Zhang, Fengmao Lv. Attention is not Enough: Mitigating the Distribution Discrepancy in Asynchronous Multimodal Sequence Fusion. Proceedings of the International Conference on Computer Vision (ICCV'21), to appear, 2021. (CCF A) [19] Lei Feng, Senlin Shu, Yuzhou Cao, Lue Tao, Hongxin Wei, Tao Xiang, Bo An, Gang Niu. Multiple-Instance Learning from Similar and Dissimilar Bags. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), to appear, 2021. (CCF A) [18] Lei Feng, Senlin Shu, Nan Lu, Bo Han, Xin Geng, Gang Niu, Bo An, Masashi Sugiyama. Pointwise Binary Classification with Pairwise Confidence Comparisons. Proceedings of the 38th International Conference on Machine Learning (ICML'21), to appear, 2021. (CCF A) [17] Yuzhou Cao, Lei Feng, Xitian Xu, Bo An, Gang Niu, Masashi Sugiyama. Learning from Similarity-Confidence Data. Proceedings of the 38th International Conference on Machine Learning (ICML'21), to appear, 2021. (CCF A) [16] Dengbao Wang, Lei Feng, Minling Zhang. Learning from Complementary Labels via Partial-Output Consistency Regularization. Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), to appear, 2021. (CCF A) [15] Zhuoyi Lin, Lei Feng*, Rui Yin, Chi Xu, Chee Keong Kwoh. GLIMG: Global and Local Item Graphs for Top-N Recommender Systems. Information Sciences (INS), to appear, 2021. (IF=6.795, 中科院一区, *通讯作者) [14] Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama. Provably consistent Partial-Label Learning. Proceedings of the 34th Annual Conference on Neural Information Processing Systems (NeurIPS'20), to appear, 2020. (CCF A) [13] Lei Feng*†, Takuo Kaneko†, Bo Han, Gang Niu, Bo An, Masashi Sugiyama. Learning with Multiple Complementary Labels. Proceedings of the 37th International Conference on Machine Learning (ICML'20), pp.3072-3081, 2020. (CCF A, *通讯作者, †共同一作) [12] Jiaqi Lv, Miao Xu, Lei Feng, Gang Niu, Xin Geng, Masashi Sugiyama. Progressive Identification of True Labels for Partial-Label Learning. Proceedings of the 37th International Conference on Machine Learning (ICML'20), pp.6500-6510, 2020. (CCF A) [11] Jun Huang*, Linchuan Xu, Jing Wang, Lei Feng*, Kenji Yamanishi. Discovering Latent Class Labels for Multi-Label Learning. Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), pp.3058-3064, 2020. (CCF A, *通讯作者) [10] Lei Feng, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, Bo An. Can Cross Entropy Loss Be Robust to Label Noise?Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), pp.2206-2212, 2020. (CCF A) [9] Hongxin Wei, Lei Feng*, Xiangyu Chen, Bo An. Combating Noisy Labels by Agreement: A Joint Training Method with Co-Regularization. Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'20), pp.13726-13735, 2020. (CCF A, *通讯作者) [8] Lei Feng, Jun Huang, Senlin Shu, Bo An. Regularized Matrix Factorization for Multi-Label Learning with Missing Labels. IEEE Transactions on Cybernetics (IEEE-TCYB), DOI: 10.1109/TCYB.2020.3016897. (IF=11.079, 中科院一区) [7] Yan Yan, Shining Li, Lei Feng*. Partial Multi-Label Learning with Mutual Teaching. Knowledge-Based Systems (KBS), DOI: 10.1016/j.knosys.2020.106624. (IF=5.921, 中科院一区, *通讯作者) [6] Lei Feng, Hongxin Wei, Qingyu Guo, Zhuoyi Lin, Bo An. Embedding-Augmented Generalized Matrix Factorization for Recommendation with Implicit Feedback. IEEE Intelligent Systems (IEEE-IS), DOI: 10.1109/MIS.2020.3036136. (IF=3.21, 中科院三区) [5] Lei Feng, Bo An. Partial Label Learning with Self-Guided Retraining. Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), pp.3542-3549, 2019. (CCF A) [4] Lei Feng, Bo An, Shuo He. Collaboration based Multi-Label Learning. Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), pp.3550-3557, 2019. (CCF A) [3] Lei Feng, Bo An. Partial Label Learning by Semantic Difference Maximization. Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), pp.2294-2300, 2019. (CCF A) [2] Shuo He, Lei Feng, Li Li. Estimating Latent Relative Labeling Importances for Multi-Label Learning. Proceedings of the 2018 IEEE Conference on Data Mining (ICDM'18), pp.1013-1018, 2018. (CCF B) [1] Lei Feng, Bo An. Leveraging Latent Label Distributions for Partial Label Learning. Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), pp.2107-2113, 2018. (CCF A)