汪波

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Research Interests

My principal research interests lie in the development of efficient algorithms and statistical models for weakly supervised and semi-supervised learning problems, as well as Graph Neural Network. I work with core machine learning methodologies, including kernel methods, generative models, probabilistic and stochastic modeling, scalable optimization algorithms, and deep learning approaches. I am also interested in developing automatic machine learning methods, as well as optimization-based models to address interdisciplinary problems. For instance, I have conducted research on the multiple criteria decision making in credit scoring and network intrusion detection. In addition, numerous artificial intelligence fields, such as reinforcement learning and probabilistic graphical models, are also favored in my study.

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Publications

♦    Preprints

• Liu J, Wang B*, Qi Z. Fast and Accurate Single Image Super-resolution via Enhanced U-Net, 2019. (Joint First Author)

 ♦    2024

• Sun S, Wang B*, Tian Y. Decoupled Representation for Multi-View Learning. Pattern Recognition, 2024. (Joint First Author) (DOI: 10.1016/j.patcog.2024.110377) (To Appear)

• Li K, Yang J, Ma S, Wang B, Wang S, Tian Y, Qi Z. Rethinking Lightweight Convolutional Neural Networks for Efficient and High-quality Pavement Crack Detection. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(1): 237-250.

♦    2023

• Liu J, Wang B*, Hang H, Wang H, Qi Z, Tian Y, Shi Y. LLP-GAN: A GAN-based Algorithm for Learning from Label Proportions. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(11): 8377-8388. (Corresponding Author) (DOI: 10.1109/TNNLS.2022.3149926)
  

• Li K, Wang B*, Qi Z, Tian Y. Fast and Accurate Road Crack Detection Based on Adaptive Cost-sensitive Loss Function. IEEE Transactions on Cybernetics, 2023, 53(2): 1051-1062. (Joint First Author) (DOI: 10.1109/TCYB.2021.3103885)

• Liu J, Hang H, Wang B*, Li B, Wang H, Tian Y, Shi Y. GAN-CL: Generative Adversarial Networks for Learning from Complementary Labels. IEEE Transactions on Cybernetics, 2023, 53(1): 236-247. (Corresponding Author) (DOI: 10.1109/TCYB.2021.3089337)

• Pan ZS, Wang B*, Zhang RB, Wang SF, Li YJ, Li Y. MIML-GAN: A GAN-based Algorithm for Multi-instance Multi-label Learning on Overlapping Signal Waveform Recognition. IEEE Transactions on Signal Processing, 2023, 17: 859-872. (Joint First Author) (DOI: 10.1109/TSP.2023.3242091)

• Wang B, Sun Y, Tong Q. LLP-AAE: Learning from Label Proportions with Adversarial Autoencoder. Neurocomputing, 2023, 537: 282-295. (DOI: 10.1016/j.neucom.2023.03.019)

♦    2022

• Liu JB, Qi ZQ, Wang B*, Tian YJ, Shi Y. SELF-LLP: Self-supervised Learning from Label Proportions with Self-ensemble. Pattern Recognition, 2022, 129: 108767. (Corresponding Author) (DOI: 10.1016/j.patcog.2022.108767)

♦    2021

• Tong Q, Sun M, Wang B*, Liu D. AutoAno: Anomaly Localization with Self-supervised Multi-scale Feature and Multivariate Gaussian Estimation. IEEE/WIC/ACM International Conference on Web Intelligence, 2021: 368-374. (Corresponding Author)

• Liu J, Wang B*, Shen X, Qi Z, Tian Y. Two-stage Training for Learning from Label Proportions. The 30th International Joint Conference on Artificial Intelligence (IJCAI-21), 2021: 2737-2743. (Joint First Author, CCF-A)
  

• Li B, Shi Y, Wang B, Qi Z, Liu J. RGSR: A Two-step Lossy JPG Image Super-resolution Based on Noise Reduction. Neurocomputing, 2021, 419: 322-334.

• Li K, Tian Y*, Wang B*, Qi Z, Wang Q. Bi-directional Pyramid Network for Edge Detection. Electronics, 2021, 10(3): 329. (Corresponding Author)

♦    2020

• Li B, Wang B, Liu J, Qi Z, Shi Y. s-LWSR: Super Lightweight Super-resolution Network. IEEE Transactions on Image Processing, 2020, 29: 8368-8380. (CCF-A)

• Shi Y, Liu J, Wang B, Qi Z, Tian Y. Deep Learning from Label Proportions with Labeled Samples. Neural Networks, 2020, 128: 73-81.

• Meng F, Qi Z, Chen Z, Wang B, Shi Y. Token Based Crack Detection. Journal of Intelligent and Fuzzy Systems, 2020, 38(3): 3501-3513.

♦    2019

• Liu J, Wang B*, Qi Z, Tian Y, Shi Y. Learning from Label Proportions with Generative Adversarial Networks. The 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 2019. (Joint First Author, CCF-A)

• Shi Y, Li B, Wang B, et al. Unsupervised Single-image Super-resolution with Multi-Gram Loss. Electronics, 2019, 8(8): 833.

• Zhang F, Liu J, Wang B, Qi Z, Shi Y. A Fast Algorithm for Multi-class Learning from Label Proportions. Electronics, 2019, 8(6): 609.

• Li H, Tong Q, Wang B*. Non-negative Matrix Factorization Based Learning from Label Proportions for Vehicle Loan Default Detection. The International Conference on Information Technology and Quantitative Management (ITQM), 2019. (Corresponding Author)

• Qian Y, Tong Q, Wang B*. Multi-class Learning from Label Proportions for Bank Customer Classification. The International Conference on Information Technology and Quantitative Management (ITQM), 2019. (Corresponding Author)

• Lu K, Zhao X, Wang B*. A Study on Mobile Customer Churn Based on Learning from Soft Label Proportions, The International Conference on Information Technology and Quantitative Management (ITQM), 2019. (Corresponding Author)

• Li B, Shi Y, Li S, Wang B, Qi Z, Liu J. A Novel Texture Generation Super Resolution Model. The International Conference on Information Technology and Quantitative Management (ITQM), 2019: 942-931.

♦    2018

• Shi Y, Liu J, Qi Z, Wang B. Learning from Label Proportions on High-dimensional Data. Neural Networks, 2018, 103: 9-18.

• Li Y, Wang B*. A Study on Customer Churn of Commercial Banks Based on Learning from Label Proportions, 2018 IEEE International Conference on Data Mining Workshop (ICDMW), 2018. (Corresponding Author)

• Yuan H, Wang B, Niu L. Kernel Extreme Learning Machine for Learning from Label Proportions. 2018 International Conference on Computational Science Workshop (ICCSW), 2018: 400-409.

♦    2017

• Chen Z, Qi Z, Wang B, Cui L, Meng F, Shi Y. Learning with Label Proportions Based on Nonparallel Support Vector Machines. Knowledge-Based Systems, 2017, 119: 126-141.

• Wang B, Shi Y. A Study on Error Correction of Multiple Criteria and Multiple Constraint Levels Linear Programming Based Classification. IEEE/WIC/ACM International Conference on Web Intelligence, 2017: 984-987. 
  

• Yu Y, Wang B, Yu X. Identifying Subscribers in Freemium E-commerce Model Based on Support Vector Classification. Procedia Computer Science, 2017, 122: 1175-1181.

♦    2016

• Qi Z, Wang B, Meng F, Niu L. Learning with Label Proportions via NPSVM. IEEE Transactions on Cybernetics, 2016, 47(10): 3293-3305.

• Qi Z, Wang B, Tian Y, Zhang P. When Ensemble Learning Meets Deep Learning: A New Deep Support Vector Machine for Classification. Knowledge-Based Systems, 2016, 107: 54-60.

♦    2015

• Qi Z, Tian Y, Niu L, Wang B. Semi-supervised Classification with Privileged Information. International Journal of Machine Learning and Cybernetics, 2015, 6(4): 667-676.

• 汪波, 聂晓伟. 基于多目标数学规划的网络入侵检测方法. 计算机研究与发展, 2015, 52(10): 2239-2246.

• Liu F, Shi Y, Wang B. World Search Engine IQ Test Based on the Internet IQ Evaluation Algorithms. International Journal of Information Technology & Decision Making, 2015, 14(2): 221–237.

• Wang B, Shi Y, Tian Y. A Triple Structure of Rough Sets Based on Selection Function. Annals of Data Science, 2015: 1-15.

• Wang B, Chen Z, Qi Z. Linear Twin SVM for Learning from Label Proportions. 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2015, 3: 56-59.

• Wang B, Shi Y, Yang Z, Ju X. An Algebra Description for Hard Clustering. Lecture Notes in Computer Science (ICDS 2015), 9208: 62-69. Springer, Cham, 2015.

• Yang Z, Shi Y, Wang B. Search Engine Marketing, Financing Ability and Firm Performance in E-commerce. Procedia Computer Science, 55: 1106-1112, 2015.

♦    2014 and Before

• Liu P, Sun J, Han L, Wang B. Research on the Construction of Macro Assets Price Index Based on Support Vector Machine. International Conference on Computational Science Workshop (ICCSW), 2014, 29: 1801-1815.

• Yang Z, Shi Y, Wang B, Yan H. Website Quality and Profitability Evaluation in Ecommerce Firms Using Two-stage DEA Model. Procedia Computer Science, 2014, 30: 4-13.

• Wang B, Wang Y. A Multiple-Criteria and Multiple-constraint Levels Linear Programming Based Error Correction Classification Model. Procedia Computer Science, 2013, 17: 1073-1082.

• Wang F, Suphamitmongkol W, Wang B. Advertisement Click-through Rate Prediction Using Multiple Criteria Linear Programming Regression Model. Procedia Computer Science, 2013, 17: 803-811.    

• Wang B, Shi Y. Error Correction Method in Classification by Using Multiple-Criteria and Multiple-constraint Levels Linear Programming. International Journal of Computers Communications and Control, 2012, 7: 976-989.

• Wang Y, Wang B, Zhang X. A New Application of the Support Vector Regression on the Construction of Financial Conditions Index to CPI Prediction. International Conference on Computational Science Workshop (ICCSW), 2012, 9: 1263-1272.

• 汪波, 石勇. 一个基于妥协解的多目标线性规划分类模型. 中国管理学年会, 2011.

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学术服务

♦ 曾经或正在担任如下期刊审稿人： 

• 计算机学报 (Chinese Journal of Computers)

• Annals of Data Science (AODS)

• Journal of Computational Science (JOCS)

• IEEE Transactions on Cybernetics (TC)

• Information Sciences (INS)

• Neurocomputing

• International Journal of Computers, Communications & Control (IJCCC)

• International Journal of Information Technology & Decision Making (IJITDM)

• Soft Computing (SOCO)

• Applied Sciences (APPLSCI)

• Electronics

• Sensors
  

♦ 曾经或正在担任如下会议审稿人： 

• Conference on Neural Information Processing Systems (NeurIPS)

• International Conference on Learning Representation (ICLR)

• AAAI Conference on Artificial Intelligence (AAAI)

• IEEE International Conference on Data Mining (ICDM)

• International Conference on Computational Science (ICCS)

• IEEE/WIC/ACM International Conference on Web Intelligence (WIC) 

• International Conference on Information Technology and Quantitative Management (ICITQM) 

• International Conference on Data Science (ICDS)

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获奖情况

• 国际信息技术与量化管理学会The International Academy of Information Technology and Quantitative Management (IAITQM)杰出贡献奖 (2015)

• 本科课堂教学质量评价按院（部）排列前10% （2021-2022学年春季学期）

• 惠园优秀青年学者 (2022-2024)

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研究项目

• 科研项目

• 国家自然科学基金委青年科学基金项目，61702099，基于PAC理论的标签比例学习算法研究，2018/01 - 2020/12，主持

• 对外经济贸易大学“惠园优秀青年学者”项目，21YQ10，基于深度学习的标签比例学习算法机器在信用卡客户分类中的应用，2022/01-2024/12，主持

• 对外经济贸易大学信息学院“管理科学与工程”学科培育专项项目，面向管理决策的非结构化图像挖掘技术研究，2018/05 - 2020/04，主持

• 对外经济贸易大学培育项目，18PY45-61702099， 大规模标签比例学习问题研究，2018/03-2019/03，主持

• 对外经济贸易大学新进教师项目，16QD17，弱标签分类问题研究，2017/01-2018/12，主持

• 国家自然科学基金委面上项目，11671379，非Lipschitz优化的高效光滑化信赖域方法及应用，2017/01-2020/12，参加

• 国家自然科学基金委重大研究计划，91546201，面向管理决策的非结构化大数据分析方法与关键技术，2016/01-2019/12，参加

• 北京市社会科学基金青年项目，17GLC052，大数据质量对北京市政府公共服务满意度提升影响机制研究，2017/09-2019/06，参加

• 国家自然科学基金委面上项目，61472390，可拓支持向量机理论、方法与应用研究，2015/01-2018/12，参加

• 国家自然科学基金委重点项目，71331005，大数据环境下的管理决策创新研究，2014/01-2018/12，参加

• 国家自然科学基金国际(地区)合作与交流项目，71110107026，最优化数据挖掘的商业智能方法以及在金融与银行管理中的应用，2012/01-2016/12，参加

• 国家自然科学基金创新群体项目，70921061，数据挖掘与知识管理：理论与应用研究，2010/01-2012/12，参加

• 商业项目：

• 资产价格指数构建研究，2011年9月至2013年3月，财政部委托中国资产评估协会同中国科学院虚拟经济与数据科学研究中心合作的课题

• 澳大利亚必和必拓公司国际交流项目：石油勘探中的数据挖掘 (Research Project Between BHP Billiton and Graduate University of Chinese Academy of Sciences: Data Mining for Petroleum Exploration)，2011.

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培养学生

博士 Ph.D Students

陈赞 2022年9月 - 

尤一玮 2024年9月 -

硕士 MS Students

金融学方向

李岳 2017年9月 - 2019年6月（基于标签比例学习的P2P网络借贷违约风险预测研究；中国工商银行数据中心）

钱亚星 2018年9月 - 2020年6月

陈阳 2019年9月 - 2021年6月

产业经济学方向

李海 2018年9月 - 2020年6月

卢凯丽 2018年9月 - 2020年6月

康丽敏 2019年9月 - 2022年6月

范子祎 2019年9月 - 2021年6月

杨欢 2019年9月 - 2021年6月

孙应特 2020年9月 - 2022年6月

管理科学与工程方向

姜超然 2021年9月 - 2023年6月

李世杰 2022年9月 - 

尤一玮 2022年9月 -

黄金龙 2022年9月 -

钟佳楠 2022年9月 -

陈彦希 2023年9月 -

李春晓 2023年9月 -

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所授课程

应用工程数学

最优化原理

统计学习理论

机器学习与数据挖掘

R语言

计算机应用基础

电子商务与贸易案例分析（留学生）

数据科学导论

数据统计分析

管理信息系统

国际服务外包理论与实务

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Created: 2017.3.15 Last Modified：2024.3.1