研究方向
  • 机器学习

研发大规模数据分析,深度学习模型构建及大规模模型训练技术,解决数据驱动的智能化问题。

  • 运筹优化

研发优化复杂系统的技术,解决现实中的复杂决策问题,如优化库存、计算、流量等资源的配置问题等。


产品及应用
  • MindOpt 优化求解器

    MindOpt优化求解器是针对解决运筹优化类问题的求解器,包含线性规划、混合整数规划、非线性规划、黑盒优化、在线优化等通用优化求解器能力,并提供优化求解器在不同行业内的数学建模和应用的解决方案案例。目前已经面向社会发布线性规划求解器,并参与国际榜单测评,得到世界领先的成绩。

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  • “达灵”计算资源优化

    达灵系统是针对计算基础设施的智能化解决方案,通过与计算资源管理系统的有效结合,使用机器学习和运筹优化技术实现更为优化的计算资源使用方案,从而提升计算基础设施的稳定性和利用率。

    达灵解决方案包括以下功能模块:

    1)智能运维,如异常检测预警和预测维保;
    2)应用画像,如使用量预测和干扰检测等;
    3)调度控制,如资源最优编排、在线调度、批量调度、重调度、负载均衡、弹性伸缩方案等;
    4)资源规划,如资源容量规划、基础设施规划及推演。 2017年双11,存储调度将资源使用降低25%,资源峰值水位降低30%;集群调度将CPU分配率从70% 拉升至并维持在90%。

  • “龙灵”零售个性化流量优化

    龙灵系统是通过分析行为构建行为表征,利用在线决策的流量进行个性化、以及流量确定性的优化处理,形成智能化的流量技术。其广泛应用于各类泛零售业务,例如天猫、优酷、盒马和闲鱼等业务;其中线上运营决策系统在天猫双11发挥重要作用,调用量超过6亿次/日。


学术成果
论文
  • Fan Yang, Kai He, Linxiao Yang, Hongxia Du, Jingbang Yang, Bo Yang, Liang Sun. "Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach",  In Advances in Neural Information Processing Systems (NeurIPS 2021).
  • Xinmeng Huang*, Kun Yuan*, Xianghui Mao, and Wotao Yin. "An Improved Analysis and Rate for Variance Reduction under Without-replacement Sampling Orders",  In Advances in Neural Information Processing Systems (NeurIPS 2021).
  • Bicheng Ying*, Kun Yuan*, Yiming Chen*, Pan Pan, and Wotao Yin. "Exponential Graph is Provably Efficient in Decentralized Deep Training", In Advances in Neural Information Processing Systems (NeurIPS 2021).
  • Kun Yuan*, Yiming Chen*, Xinmeng Huang*, Yingya Zhang, Pan Pan, Yinghui Xu, and Wotao Yin. "DecentLaM: Decentralized Momentum SGD for Large-Batch Deep Training", in Proc. of International Conference on Computer Vision (ICCV 2021).
  • Junhao Hua; Ling Yan; Huan Xu; Cheng Yang. "Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach," in Proc. of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). 
  • Hanqin Cai, Yuchen Lou, Daniel McKenzie, and Wotao Yin.“A Zeroth-Order Block Coordinate Descent Algorithm for Huge-Scale Black-Box Optimization.”In Proc. of The 38th International Conference on Machine Learning (ICML 2021).
  • Haoxian Chen, Ziyi Huang, Henry Lam, Huajie Qian, and Haofeng Zhang. "Learning prediction intervals for regression: Generalization and calibration." In International Conference on Artificial Intelligence and Statistics, pp. 820-828. (AISTATS 2021)
  • Qingsong Wen, Kai He, Liang Sun, Yingying Zhang, Min Ke, and Huan Xu, "RobustPeriod: Time-Frequency Mining for Robust Multiple Periodicities Detection," in Proc. ACM SIGMOD International Conference on Management of Data (SIGMOD 2021), Xi'an, China, Jun. 2021.
  • Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Junkun Gao, Xue Wang, and Huan Xu, "Time Series Data Augmentation for Deep Learning: A Survey," in Proc. 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), Montreal, Canada, Aug. 2021.
  • Qingyang Xu, Qingsong Wen, Liang Sun, A Two-Stage Framework for Seasonal Time Series Forecasting, in Proc. of IEEE 46th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, Canada, June 2021
  • Linxiao Yang, Qingsong Wen, Bo Yang, Liang Sun, A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition, in Proc. of IEEE 46th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2021), Toronto, Canada, June 2021.
  • Qingsong Wen, Zhengzhi Ma, and Liang Sun, "On Robust Variance Filtering and Change Of Variance Detection," accepted for Oral Presentation, in Proc. of IEEE 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2020), Barcelona, Spain, May 2020. Oral paper.
  • Qingsong Wen, Zhe Zhang, Yan Li and Liang Sun, "Fast RobustSTL: Efficient and Robust Seasonal-Trend Decomposition for Time Series with Complex Patterns," in Proc. of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2020), San Diego, CA, Aug. 2020
  • Yifei Zhao, Yu-Hang Zhou, Mingdong Ou, Huan Xu, Nan Li: Maximizing Cumulative User Engagement in Sequential Recommendation: An Online Optimization Perspective. KDD 2020: 2784-2792
  • Guillermo Gallego, Anran Li, Van-Anh Truong, and Xinshang Wang. 2020. “Approximation Algorithms for Product Framing and Pricing.” Operations Research 68 (1): 134-60.
  • Rong Jin, David Simchi-Levi, Li Wang, Xinshang Wang, and Sen Yang. 2019. “Shrinking the Upper Confidence Bound: A Dynamic Product Selection Problem for Urban Warehouses.” SSRN Electronic Journal.
  • David Simchi-Levi, Rui Sun, and Xinshang Wang. 2019. “Online Matching with Bayesian Rewards.” SSRN Electronic Journal.
  • Zhao Kui, Junhao Hua, Ling Yan, Qi Zhang, Huan Xu, and Cheng Yang. "A Unified Framework for Marketing Budget Allocation." In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1820-1830. 2019.
  • Yi Peng, Miao Xie, Jiahao Liu, Xuying Meng, Nan Li, Cheng Yang, Tao Yao. A Practical Semi-Parametric Contextual Bandit. International Joint Conference on Artificial Intelligence 2019 (IJCAI)
  • Mingdong Ou, Nan Li, Cheng Yang, Shenghuo Zhu, and Rong Jin. Semi-parametric sampling for stochasitc bandits with many arms. In Proc. of AAAI Conference on Artificial Intelligence 2019 (AAAI)
  • Yu-Hang Zhou, Chen Liang, Nan Li, Cheng Yang, Shenghuo Zhu, Rong Jin. Robust Online Matching with User Arrival Distribution Drift. In Proc. of AAAI Conference on Artificial Intelligence 2019 (AAAI)
  • Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Huan Xu, Shenghuo Zhu. "RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series," in Proc. of the 33rd AAAI Conference on Artificial Intelligence (AAAI 2019), 2019, pp. 5409-5416, Honolulu, Hawaii, Jan. 2019. Oral paper.
  • Qingsong Wen, Jingkun Gao, Xiaomin Song, Liang Sun, Jian Tan. "RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend Filtering," in Proc. of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019), pp. 3856-3862, Macao, China, Aug. 2019. Oral paper.
  • Hao Yu and Sen Yang and Shenghuo Zhu, ‘‘Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning," AAAI Conference on Artificial Intelligence (AAAI 2019)
  • Hao Yu and Rong Jin and Sen Yang, ‘‘On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization," International Conference on Machine Learning (ICML 2019)
  • Hao Yu and Rong Jin ‘‘On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization," International Conference on Machine Learning (ICML 2019)
  • Cong Leng, Hao Li, Shenghuo Zhu, Rong Jin. Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM. In: Proceedings of the 32rd AAAI Conference on Artificial Intelligence (AAAI, 18), New Orleans, LA, 2018.
  • Ao Zhang, Nan Li, Jian Pu, Jun Wang, Junchi Yan, Hongyuan Zha. tau-FPL: Tolerance-Constrained Learning in Linear Time. In: Proceedings of the 32rd AAAI Conference on Artificial Intelligence (AAAI, 18), New Orleans, LA, 2018.
  • Qi Qian, Jisheng Tang, Hao Li, Shenghuo Zhu and Rong Jin. Large-scale Distance Metric Learning with Uncertainty. In: Proceedings of the 31th IEEE Conference on Computer Vision and Pattern Recognition (CVPR, 18), Salt Lake City, UT, 2018.
  • Mingdong Ou, Nan Li, Shenghuo Zhu, Rong Jin. Multinomial Logit Bandit with Linear Utility Functions. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI, 18), 2018.
  • Yang Yu, Wei-Yang Qu, Nan Li, and Zimin Guo. Open category classification by adversarial sample generation. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI, 17), Melbourne, Australia, 2017.
  • Yiming Chen*, Kun Yuan*, Yingya Zhang, Pan Pan, Yinghui Xu, and Wotao Yin. "Accelerating Gossip SGD with Periodic Global Averaging," in Proc. of The 38th International Conference on Machine Learning (ICML 2021).
  • Qingsong Wen, Liang Sun, Fan Yang, Xiaomin Song, Junkun Gao, Xue Wang, and Huan Xu, "Time Series Data Augmentation for Deep Learning: A Survey," in Proc. 30th International Joint Conference on Artificial Intelligence (IJCAI 2021), Montreal, Canada, Aug. 2021.
展开更多
竞赛
  • 2021: KDD Cup, Multi-dataset Time Series Anomaly Detection, Top 1%
  • 2021: General Language Understanding Evaluation (GLUE) Benchmark, First Place
  • 2021: International AIOps Challenge, Runner-up
  • 2020: Linear Programming Benchmark, First Place
  • 2018: PASCAL VOC Target Detection Competition, First Place

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