姓 名：冯磊
性 别：男
职 称：教授 (博导/硕导、弘深青年学者)
办公地点：主教1507
E-mail: lfeng@cqu.edu.cn
Homepage: https://lfeng-ntu.github.io/
研究方向：机器学习，数据挖掘，人工智能
招生信息：年度招收博士生1名、硕士生3名，招收数学、计算机等专业
2 个人简介冯磊，重庆大学弘深青年学者引进人才（教授、博导），兼任日本理化学研究所先进智能研究中心（RIKEN Center for Advanced Intelligence Project）Visiting Scientist。博士毕业于新加坡南洋理工大学（Nanyang Technological University, Singapore），在提前毕业的情况下，获得南洋理工大学计算机科学与工程学院杰出博士学位论文奖第二名（NTU SCSE Outstanding PhD Thesis Award Runner-Up）。中国计算机学会（CCF）会员，中国人工智能学会（CAAI）会员，国际人工智能促进学会（AAAI）会员，美国计算机学会（ACM）会员，中国人工智能学会机器学习专委会通讯委员。担任IJCAI 2021与AAAI 2022高级程序委员会委员（senior program committee member），ICML 2021 专家审稿人（expert reviewer），以及其他国际顶级（CCF A类）会议（包括NeurIPS、KDD、CVPR、ICCV、AAAI）的程序委员会委员/审稿人，并受邀担任多个国际顶级期刊（包括JMLR、IEEE-TPAMI、IEEE-TIP、IEEE-TNNLS、MLJ）审稿人。
主要研究方向为机器学习、数据挖掘、人工智能。已在International Conference on Machine Learning (ICML)，Annual Conference on Neural Information Processing Systems (NeurIPS), ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)，IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), International Conference on Computer Vision (ICCV), AAAI Conference on Artificial Intelligence (AAAI), International Joint Conference on Artificial Intelligence (IJCAI)等国际顶级（CCF A类）会议与中科院一区期刊上发表论文二十余篇。
本课题组研究经费充足，与国内外著名高校和研究机构有紧密的合作，欢迎青年老师和有意从事学术研究的博士后博士生硕士生加入（或访问）本课题组
招收弘深青年教师（特别资助：37-40万元/年，重点资助：27-30万元/年）
注意：2022年秋季入学的博士硕士研究生招生名额已满，谢谢各位同学的热情，请勿再邮件联系我了
3 学术成果代表性论文
[22] Lue Tao, Lei Feng, Jinfeng Yi, Shengjun Huang, Songcan Chen. Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training. Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21), to appear, 2021. (CCF A)
[21] Dengbao Wang, Lei Feng, Minling Zhang. Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence. Proceedings of the 35th Annual Conference on Neural Information Processing Systems (NeurIPS'21), to appear, 2021. (CCF A)
[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)
4 学术服务会议专家审稿人（expert reviewer）：ICML 2021
会议高级程序委员会委员（SPC）：IJCAI 2021、AAAI 2022
会议程序委员会委员/审稿人（PC/reviewer）：NeurIPS 2020-2021、CVPR 2021-2022、KDD 2021、ICCV 2021、ACML 2021、ICLR 2021-2022、AAAI 2020-2021、AISTATS 2022、SDM 2022
期刊审稿人：JMLR、MLJ、IEEE-TPAMI、IEEE-TIP、IEEE-TNNLS、IEEE-TMM、IEEE-IS、KAIS、KBS、APIN