研究方向
  • 大规模图模型及图嵌入模型

基于企业知识图谱,运营数据,工作流的企业智能分析和决策支持引擎以及金融知识图谱的金融智能推理和决策支持引擎,来研究图模型及深度学习在金融场景下的网络和图上的大规模算法和创新应用,包括万亿级资金交易网络的推荐,营销和风控问题。

  • 机器学习的数据安全和隐私保护

通过对隐私保护,信息安全和机器学习的结合以及多方金融数据源在互不共享数据情况下的分布式建模和分析的研究,不断提升其在金融多方数据利用能力。


产品及应用
  • 金融大脑

    基于蚂蚁金服强大的技术积累,运用AI领域技术来形成金融领域的核心人工智能能力,赋能蚂蚁金服内部智能营销、智能安全、智能金融信息服务等能力,并通过平台化的产品,与生态伙伴和金融机构达成战略合作。

    企业图谱(风报):一款基于NLP处理技术的企业金融智能信息服务系统,涵盖工商、诉讼、税务、行政处罚、投融资、高管变动、新闻事件等26大类企业情报信息。“风报”包含全国近4,200万家工商登记主体信息,汇聚全网超过4万个数据来源的近10亿条行政公示、审判流程、企业信息披露和新闻媒体报道,并通过人工智能技术将非结构化的文本进行清洗、分析、关联,形成结构化的企业情报信息。自2016年上线以来,其已经在企业尽职调查、风险控制、合规审计、司法调查、行业研究和商机及投资机会筛选等场景上进行了广泛的应用,涵盖商业银行、保险、融资租赁、供应链金融、私募股权投资等典型客户。

     

  • 智能客服(零号云客服)

    零号云客服由蚂蚁金融云和阿里云平台联合推出,目前已服务包括证券、保险、银行在内的3000余家金融机构。它是人工智能+公有云+云客服的综合解决方案(或私有云产品的部署),有效解决传统客服中心成本高、效率低等问题。目前,在不同行业应用中提供的自助服务占比高达85%,并实现对用户来电、文字问答的24小时实时响应。

  • 智能助理(小钻风)

    “小钻风”是通过语音、自然语言、图像与视觉、情感识别、人机对话等技术,结合相应的场景去理解用户信息、问题、意图与情感,以实现自然的人机交互及用户需求与服务的准确连接,进而提供一站式金融生活助理服务,并以平台的形式开放给行业合作伙伴。


研究团队
漆远达摩院金融智能实验室负责人

蚂蚁金服副总裁,麻省理工学院博士,曾任普渡大学计算机科学系和统计系终身副教授。

宋乐达摩院金融智能实验室研究员

悉尼大学博士,世界知名机器学习专家,曾任佐治亚理工学院终身副教授。


学术成果
论文
  • Yisen Wang, Weiyang Liu, Xingjun Ma, James Bailey, Hongyuan Zha, Le Song, and Shu-Tao Xia. Iterative learning with open-set noisy labels. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018
  • Bo Dai, Albert Shaw, Niao He, Lihong Li, and Le Song. Boosting the actor with dual critic. In International Conference on Learning Representations (ICLR), 2018
  • Bo Dai, Albert Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, and Le Song. Sbeed: Convergent reinforcement learning with nonlinear function approximation. International Conference on Machine Learning (ICML), 2018.
  • Hajun Dai, Zornitsa Kozareva, Bo Dai, Alex Smola, and Le Song. Learning steady states of iterative algorithms over graphs. ICML, 2018.
  • Weiyang Liu, Bo Dai, Xingguo Li, James Rehg, and Le Song. Towards black-box iterative machine teaching. ICML, 2018.
  • Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. Adversarial attack on graph structured data. ICML, 2018.
  • Jianfei Chen, Jun Zhu, and Le Song. Stochastic training of graph convolutional networks. ICML, 2018.
  • Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan. Learning to explain: An information-theoretic perspective on model interpretation. ICML, 2018.
  • Woosang Lim, Rundong Du, Bo Dai, Kyomin Jung, Le Song, and Haesun Park. Multi-scale nystrom method. Artificial Intelligence and Statistics (AISTATS), 2018.
  • Yichen Wang, Evangelos Theodorou, Apurv Verma, and Le Song. A stochastic differential equation framework for guiding online user activities in closed loop. AISTATS, 2018.
  • Hanjun Dai, Yingtao Tian, Bo Dai, Steven Skiena, and Le Song. Syntax-directed variational autoencoder for structured data. International Conference on Learning Representations (ICLR), 2018.
  • Weiyang Liu, Zhen Liu, Zhiding Yu, Bo Dai, Rongmei Lin, Yisen Wang, James Rehg, and Le Song. Decoupled networks. CVPR, 2018.
  • Y. Zhang, H. Dai, Z. Kozareva, A. Smola, and L. Song. Variational reasoning for question answering with knowledge graph. AAAI Conference on Artificial Intelligence (AAAI), 2018.
  • K. Kawaguchi, B. Xie, V. Vikas, and L. Song. Deep semi-random features for nonlinear function approximation. AAAI, 2018.
  • S. Xiao, M. Farajtabar, X. Ye, J. Yan, L. Song, and H. Zha. Learning conditional generative models for temporal point processes. AAAI, 2018.
  • Privacy Preserving Point-of-interest Recommendation Using Decentralized Matrix Factorization. Chaochao Chen, Ziqi Liu, Peilin Zhao, Jun Zhou, Xiaolong Li. AAAI, 2018.
  • Shaosheng Cao, Wei Lu, Jun Zhou and Xiaolong Li. cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information. AAAI, 2018.
  • Liu, Q., Xiang, B., Yuan, N. J., Chen, E., Xiong, H., Zheng, Y., & Yang, Y. (2017). An influence propagation view of PageRank. ACM, 11(3), 30.
  • Distributed Collaborative Hashing and Its Applications in Ant Financial. Chaochao Chen, Ziqi Liu, Peilin Zhao, Longfei Li, Jun Zhou, Xiaolong Li. KDD, 2018.
  • A Sparsity Guided Deep Framework for Cross-Domain and Cross-System Recommendations. Feng Zhu, Yan Wang, Chaochao Chen, Mehmet Orgun, Jia Wu, Guanfeng Liu. IJCAI, 2018.
  • Weiran Huang, Jungseul Ok, Liang Li, Wei Chen. Combinatorial Pure Exploration with Continuous and Separable Rewards and Its Applications. IJCAI, 2018.
  • Zhiqiang Zhang, Chaochao Chen, Jun Zhou and Xiaolong Li. An Industrial-scale System for Heterogeneous Information Card Ranking in Alipay. DASFAA, 2018.
  • Chuan Shi, Zhiqiang Zhang, Yugang Ji, Weipeng Wang, Philiph S. Yu and Zhiping Shi. SemRec: A Personalized Semantic Recommendation Method based on Weighted Heterogeneous Information Networks. WWW, 2018.
  • Ya-Lin Zhang, Longfei Li, Jun Zhou, Xiaolong Li, Zhi-Hua Zhou. Anomaly Detection with Partially Observed Anomalies. WWW, April 23-27, 2018, Lyon, France.
  • Wenjing Fang, Jun Zhou, Xiaolong Li, and Kenny Q. Unpack Local Model Interpretation for GBDT. ZhuDASFAA(International Conference on Database Systems for Advanced Applications.
  • Cen Chen, Yinfei Yang, Jun Zhou, Xiaolong Li and Forrest Sheng Bao. Cross-Domain Review Helpfulness Prediction Based on Convolutional Neural Networks with Auxiliary Domain Discriminators. NAACL, 2018.
  • Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha. Learning Deep Hidden Nonlinear Dynamics from Aggregate Data. UAI, 2018.
  • Ziqi Liu,Chaochao Chen, Xinxing Yang, Jun Zhou, Xiaolong Li, Le Song. Heterogeneous Graph Neural Networks for Malicious Account Detection. CIKM, 2018.
  • H. Dai, E. Khalil, Y. Zhang, B. Dilkina, and L.Song. Learning combinatorial optimization algorithms over graphs. Neural Information Processing Systems (NIPS), 2017.
  • S. Xiao, M. Farajtabar, X. Ye, J. Yan, L. Song, and H. Zha. Wasserstein learning of deep generative point process models. NIPS, 2017.
  • W. Liu, Y. Zhang, X. Li, Z. Yu, B. Dai, T. Zhao, and L. Song. Deep hyperspherical learning of convolution neural networks. NIPS, 2017.
  • L. Song, S. Vempala, J. Wilmes, and B. Xie. On the complexity of learning neural networks. NIPS, 2017.
  • Y. Wang, X. Ye, H. Zha, and L. Song. Predicting user activity level in point process models with mass transport equation. NIPS, 2017.
  • X. Xu, C. Liu, Q. Feng, H. Yin, L. Song, and D. Song. Neural network-based graph embedding for cross-platform binary code similarity detection. ACM Conference on Computer and Communications Security (CCS), 2017.
  • Kun He, Liang Li, Xingwu Liu, Yuyi Wang, Mingji Xia. Variable Version Lovász Local Lemma: Beyond Shearer’s Bound. FOCS, 2017.
  • Weiran Huang, Liang Li and Wei Chen. Partitioned Sampling of Public Opinions Based on Their Social Evolution. AAAI, 2017.
  • Ziqi Liu, Alex Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng, Jun Zhou. Attributing hacks with survival trend filtering. Electronic Journal of Statistics, 2017.
  • Wenpeng Zhang, Peilin Zhao, Wei Liu, Steven Hoi, Wenwu Zhu, Tong Zhang. Projection-Free Distributed Online Learning in Networks. ICML, 2017.
  • Jun Zhou, Xiaolong Li, Peilin Zhao, Chaochao Chen, Longfei Li, Xinxing Yang, Qing Cui, Jin Yu, Xu Chen, Yi Ding and Yuan Qi.KunPeng: Parameter Server based Distributed Learning Systems and Its Applications in Alibaba and Ant Financial. KDD, 2017.
  • Chenghao liu, Teng Zhang, Peilin Zhao, Jun Zhou, Jianling Sun. Locally Linear Factorization Machines. IJCAI, 2017.
  • Yong Liu, Peilin Zhao, Xin Liu, Min Wu, Lixin Duan, Xiaoli Li. Learning User Dependencies for Recommendation. IJCAI, 2017.
  • Shuji Hao, Peilin Zhao, Yong Liu, Steven Hoi, Chunyan Miao. Online Multitask Relative Similarity Learning. IJCAI, 2017.
  • Peng Yang, Peilin Zhao, Xin Gao. Robust Online Multi-Task Learning with Correlative and Personalized Structures. IEEE Transactions on Knowledge and Data Engineering (TKDE), June 2017.
  • Ziqi Liu, Alex Smola, Kyle Soska, Yu-Xiang Wang, Qinghua Zheng. Attributing Hacks. AISTATS, 2017.
  • Hanjun Dai, Hui Li, Tian Tian, Xin Huang, Lin Wang, Jun Zhu, and Le Song. Adversarial attack on graph structured data. ICML, 2018.
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