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。

  • 智能时序

研发通用的智能时序算法,利用人工智能技术解决真实场景中的复杂时序问题,如时序预测、时序异常检测等。

  • 智能运维

研发大规模高效智能运维算法,利用人工智能技术解决故障检测,根因分析,预测性维护,资源弹性伸缩与优化等问题。

  • 可解释机器学习

研发自身可解释的机器学习模型,为给定白盒和黑盒模型提供解释来揭示其决策过程,达到为高风险决策场景提供可信人工智能技术,并鼓励人机交互的目的。


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。

    了解更多
  • “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。

  • 绿色能源AI · 负荷与新能源功率预测系统

    负荷与新能源功率预测系统,深度融合达摩院时序预测能力、可解释AI和自学习技术,为电网调度部门和新能源电站构建高精准预测服务。为电网科学调度提供决策依据,保障电网安全稳定运行;提高发电站功率精度,降低上报考核成本,支撑电网平衡及电力现货交易。

    软件包括以下功能模块:

    1)负荷预测,智能预测母线及系统负荷曲线,提供高精度预测结果,支撑母线安全校核,保障电网稳定运行及经济调度。

    达摩院联合山东电网,在“中国太阳城”德州市落地高精度电网负荷预测模型,覆盖近60条220千伏母线,线上整体准确率98%,达到专家级别,超过96%的考核标准。

    2)新能源功率预测,基于高精度数值气象预报和气象预报修正技术,精准预测风电场及光伏电站发电功率,有效减少考核电量,支撑电网电力平衡,提升新能源消纳能力。

    3)可解释AI智能归因,将预测日与历史参照日负荷进行差异化分析,将温度、辐照度、风速、节假日等维度上的差异映射为负荷值的差异,打开“算法黑箱”,便于调度人员理解和信任AI预测结果,大幅提升工作效率。

    注:达摩院重点布局绿色能源AI系列产品及应用,助力国家“智慧能源”及“双碳”战略落地,加速人工智能与能源产业融合进程。决策智能实验室将前沿AI决策技术,注入精准电力预测、秒级调度控制、智能电力交易三大板块。助力新型电力系统建设,解决绿色能源大规模并网难、消纳率低的问题,创造一张智能弹性的电网。


学术成果
论文和学术报告
  • Tian Zhou, Ziqing Ma, Xue Wang, Qingsong Wen, Liang Sun, Tao Yao, Wotao Yin, Rong Jin, "FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting", in Proc. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, Dec. 2022.
  • Chenxiao Yang, Qitian Wu, Qingsong Wen, Zhiqiang Zhou, Liang Sun, Junchi Yan, "Variational Context Adjustment for Temporal Event Prediction under Distribution Shifts," in Proc. 36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, Dec. 2022.
  • Kun Yuan, Xinmeng Huang, Yiming Chen, Xiaohan Zhang, Yingya Zhang, PAN PAN, "Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization", in Proc. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, Dec. 2022.
  • Xinmeng Huang, Yiming Chen, Wotao Yin, Kun Yuan, "Communication-Efficient Topologies for Decentralized Learning with O(1) Consensus Rate", in Proc. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, Dec. 2022.
  • Zhuoqing Song, Weijian Li, Kexin Jin, Lei Shi, Ming Yan, Wotao Yin, Kun Yuan, "Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression", in Proc. 36th Conference on Neural Information Processing Systems (NeurIPS 2022), New Orleans, LA, Dec. 2022.
  • Xiaolong Ma, Minghai Qin, Fei Sun, Zejiang Hou, Kun Yuan, Yi Xu, Yanzhi Wang, Yen-Kuang Chen, Rong Jin, Yuan Xie, "Effective Model Sparsification by Scheduled Grow-and-Prune Methods", Proceedings of the International Conference on Learning Representations (ICLR 2022), 2022.
  • Zejiang Hou, Minghai Qin, Fei Sun, Xiaolong Ma, Kun Yuan, Yi Xu, Yen-Kuang Chen, Rong Jin, Yuan Xie, Sun-Yuan Kung, "CHEX: CHannel EXploration for CNN Model Compression", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2022), New Orleans, 2022.
  • Tianlong Chen, Xiaohan Chen, Wuyang Chen, Howard Heaton, Jialin Liu, Zhangyang Wang, Wotao Yin. "Learning to optimize: A primer and a benchmark." Accepted by Journal of Machine Learning Research (JMLR), 2022.
  • Yi Wang, Chien-fei Chen, Peng-Yong Kong, Husheng Li, and Qingsong Wen, “A Cyber-Physical-Social Perspective on Future Smart Distribution Systems,” Proceedings of the IEEE (PIEEE 2022), 2022.
  • Xiaomin Song, Qingsong Wen, and Liang Sun, "Robust Time Series Dissimilarity Measure for Outlier Detection and Periodicity Detection,” in Proc. 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), Atlanta, GA, Oct. 2022.
  • Chaoli Zhang, Tian Zhou, Qingsong Wen, Liang Sun, "TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Freq Analysis,” in Proc. 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), Atlanta, GA, Oct. 2022.
  • Jihai Zhang, Fangquan Lin, Cheng Yang, Ziqiang Cui. "A Mental Model with Enhanced Graph Neural Networks for E-commerce Recommendation." Accepted in CIKM 2022.
  • Qia Ding, Jun Ye, Mengchang Wang, Yantao Zhang, Chunling Lu, Wen Wang, "An Integer Variable Reduction and Neighborhood Search Cutting Plane Method for Security Constrained Unit Commitment", Proceedings of the International Conference on Power and Energy Technology (ICPET 2022).
  • Yiyang Liu, Zaiwen Wen, and Wotao Yin. "A Multiscale Semi-Smooth Newton Method for Optimal Transport." Journal of Scientific Computing 91.2 (2022): 1-29.
  • HanQin Cai, Daniel McKenzie, Wotao Yin, and Zhenliang Zhang. “A One-Bit, Comparison-Based Gradient Estimator.” Applied and Computational Harmonic Analysis (September 2022): 242-66.
  • Jinshan Zeng, Wotao Yin, and Ding-Xuan Zhou. “Moreau Envelope Augmented Lagrangian Method for Nonconvex Optimization with Linear Constraints.” Journal of Scientific Computing 91, no. 2 (May 2022): 61.
  • HanQin Cai, Daniel McKenzie, Wotao Yin, and Zhenliang Zhang. “Zeroth-Order Regularized Optimization (ZORO): Approximately Sparse Gradients and Adaptive Sampling.” SIAM Journal on Optimization 32, no. 2 (June 2022): 687-714.
  • Lisang Ding, Wuchen Li, Stanley Osher, and Wotao Yin. “A Mean Field Game Inverse Problem.” Journal of Scientific Computing 92, no. 1 (July 2022): 7.
  • Sulaiman A. Alghunaim, and Kun Yuan, "A Unified and Refined Convergence Analysis for Non-Convex Decentralized Learning", accepted by IEEE Transactions on Signal Processing (TSP), June. 2022
  • Kun Yuan, Zhaoxian Wu, Qing Ling, "A Byzantine-Resilient Dual Subgradient Method for Vertical Federated Learning", in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022), May. 2022
  • Qingsong Wen, Linxiao Yang, Tian Zhou, Liang Sun, "Robust Time Series Analysis and Applications: An Industrial Perspective," in the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2022), Washington DC, Aug. 2022 (Tutorial)
  • Qingsong Wen, Linxiao Yang, Tian Zhou, Liang Sun, “Robust Time Series Analysis: from Theory to Applications in the AI Era,” in the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022), Vienna, Austria, Jul. 2022 (Tutorial)
  • Yanwei Fu, Xinwei Sun, Yao Yuan, Wotao Yin, "Sparsity Learning in Neural Networks and Robust Statistical Analysis", in the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR 2022), New Orleans, LA, Jun. 2022 (Tutorial)
  • Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, Rong Jin, "FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting," in the 39th International Conference on Machine Learning (ICML 2022), Baltimore, Maryland, July 17-23, 2022.
  • Weiqi Chen, Wenwei Wang, Bingqing Peng, Qingsong Wen, Tian Zhou, Liang Sun, "Learning to Rotate: Quaternion Transformer for Complicated Periodical Time Series Forecasting", in the 28th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD 2022), Washington DC, Aug. 2022
  • Yihong Zhou, Zhaohao Ding, Qingsong Wen, Yi Wang, "Robust Load Forecasting towards Adversarial Attacks via Bayesian Learning," IEEE Transactions on Power Systems (TPS), 2022.
  • Longyuan Li, Junchi Yan, Qingsong Wen, Yaohui Jin, and Xiaokang Yang, "Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series," IEEE Transactions on Knowledge and Data Engineering (TKDE), 2022.
  • Huajie Qian, Qingsong Wen, Liang Sun, Jing Gu, Qiulin Niu, Zhimin Tang, "RobustScaler: QoS-Aware Autoscaling for Complex Workloads," in Proc. IEEE 38th International Conference on Data Engineering (ICDE 2022), Kuala Lumpur, Malaysia, May 2022.
  • Wei Wang, Liangzhu Ge, Jingqiao Zhang, Cheng Yang. "Improving Contrastive Learning of Sentence Embeddings with Case-Augmented Positives and Retrieved Negatives," in Proc. 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), Madrid, Spain, July, 2022.
  • Jihai Zhang, Fangquan Lin, Cheng Yang, Wei Wang. "Deep Multi-Representational Item Network for CTR Prediction," in Proc. 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), Madrid, Spain, July, 2022.
  • Jihai Zhang, Fangquan Lin, Cheng Yang, Wei Jiang. "A new sequential prediction framework with spatial-temporal embedding," in Proc. 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2022), Madrid, Spain, July, 2022.
  • Yan Li, Rui Xia, Chunchen Liu, Liang Sun, "A Hybrid Causal Structure Learning Algorithm for Mixed-type Data," In Proc. of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), 2022.
  • Shuang Li, Qiuwei Li, Local and Global Convergence of General Burer-Monteiro Tensor Optimizations, In Proc. of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), 2022.
  • Yifei Xu*, Jingqiao Zhang*, Ru He*, Liangzhu Ge*, Chao Yang, Cheng Yang, Ying Nian Wu, SAS: Self-Augmentation Strategy for Language Model Pre-Training, In Proc. of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), 2022.
  • Samy Wu Fung, Howard Heaton, Qiuwei Li, Daniel McKenzie, Stanley Osher, Wotao Yin, JFB: Jacobian-free backpropagation for implicit networks, In Proc. of the 36th AAAI Conference on Artificial Intelligence (AAAI 2022), 2022.
  • Chaoli Zhang^, Zhiqiang Zhou^, Yingying Zhang^, Linxiao Yang^, Kai He^, Qingsong Wen^, Liang Sun^ (^Equally Contributed), "NetRCA: An Effective Network Fault Cause Localization Algorithm," in Proc. IEEE 47th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022), Singapore, May 2022.
  • Jihai Zhang*, Fangquan Lin*, Wei Jiang*, Ziqiang Cui*, Cheng Yang*, Gaoge Liu, Neighbor-Augmented Transformer-based Embedding for Retrieval, in Proc. IEEE 47th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2022), Singapore, May 2022.
  • Yingying Zhang, Zhengxiong Guan, Huajie Qian, Leili Xu, Hengbo Liu, Qingsong Wen, Liang Sun, Junwei Jiang, Lunting Fan and Min Ke, "CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms," in Proc. 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), 2021.
  • Kan Wu, Edward Huang, Mengchang Wang, Meimei Zheng. "Job scheduling of diffusion furnaces in semiconductor fabrication facilities", European Journal of Operational Research, 2021,
  • Tianyi Chen, Yuejiao Sun, Wotao Yin. "Solving stochastic compositional optimization is nearly as easy as solving stochastic optimization", IEEE Transactions on Signal Processing, 69, 4937-4948, 2021.
  • Tianyi Chen, Yuejiao Sun, Wotao Yin. "Communication-Adaptive Stochastic Gradient Methods for Distributed Learning", IEEE Transactions on Signal Processing, 69, 4637-4651, 2021.
  • Xinwei Zhang, Mingyi Hong, Sairaj Dhople, Wotao Yin, Yang Liu. "FedPD: A Federated Learning Framework With Adaptivity to Non-IID Data", IEEE Transactions on Signal Processing, 69, 6055-6070, 2021.
  • Hanqin Cai, Jialin Liu, and Wotao Yin. "Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection", In Advances in Neural Information Processing Systems (NeurIPS 2021).
  • Xiaohan Chen, Jialin Liu, Zhangyang Wang, and Wotao Yin. "Hyperparameter Tuning is All You Need for LISTA", In Advances in Neural Information Processing Systems (NeurIPS 2021).
  • Tianyi Chen, Yuejiao Sun, and Wotao Yin. "Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems", In Advances in Neural Information Processing Systems (NeurIPS 2021).
  • 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). 
  • 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).
  • 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.
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竞赛
  • 2022: 国际信号处理大挑战比赛 (ICASSP Grand Challenges), 网络故障定位根因分析,第一名
  • 2022: GECCO国际进化计算比赛, 基于风险的能源调度, 第一名
  • 2022: 国际知识发现和数据挖掘竞赛(KDD Cup), 空间动态风电功率预测, 前1%
  • 2021: NeurIPS机器学习组合优化国际比赛, 配置赛道, 亚军
  • 2021: NeurIPS机器学习组合优化国际比赛, 主赛道, 季军
  • 2021: 国家电网调控AI创新大赛, 电网运行组织智能安排, 冠军
  • 2021: 国家电网调控AI创新大赛, 新能源发电预测, 亚军
  • 2021: 通用自然语言理解评估, 第一名
  • 2021: 国际知识发现和数据挖掘竞赛(KDD Cup), 多数据集时间序列异常检测, 前1%
  • 2021: 国际智能运维挑战赛, 亚军
  • 2020: 优化求解器国际基准评估, 线性规划, 第一名
  • 2018: PASCAL VOC目标检测国际挑战赛, 第一名

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