Kan Ren (任侃)
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Kan Ren, Senior Researcher Office: 43rd Floor, International AI Tower, |
I'm now looking for prospective student interns (preferably more than 3 months) to work together on sequence data modeling with applications in healthcare and finance, and (sequential) decision making optimization with cutting-edge machine learning technologies. Please drop me an email with your resume if you are interested.
Our book "Recommender System: Frontiers and Practices" has been published in JD! Please read our intro.
2023.3 - The website of our SeqML research (machine intelligence on sequence data) has been online.
2023.1 - One paper about enseble learning for pretrained models has been accepted in ICLR 2023.
2022.11 - Two papers about spatial-temporal data mining and efficient ensemble learning have been accepted in AAAI 2023.
2022.9 - Two papers about AutoRL and Transformer in offline RL have been accepted in NeurIPS 2022.
2022.4 - One paper about policy ensemble in reinforcement learning has been accepted in IJCAI 2022.
2021.8 - Two papers about sequential user modeling and sequence data generation have been accepted in CIKM 2021 and ICDM 2021.
2021.2 - Latest English article about our AAAI 2021 paper! (MSR Blog)
2021.1 - Latest Chinese article about our quantitative finance research! (WeChat, MSRA news)
2020.12 - One paper about quantitative finance has been accepted in AAAI 2021.
2020.4 - I'll serve as a co-chair for 2nd workshop on Practice of Deep Learning on High-dimensional Sparse Data in KDD 2020.
2020.4 - Two papers about unbiased learning and knowledge graph-enhanced recommendation have been accepted in SIGIR 2020.
2019.10 - One paper about sequential recommendation has been accepted in WSDM 2020 (AC rate: 15%).
2019.7 - I will join Machine Learning Group of Microsoft Research Asia.
2019.4 - One paper has been accepted in Research Track of KDD 2019 as an oral presentation (AC rate oral/total: 9.1/14.1%).
2018.12 - Two papers have been accepted in AAAI 2019 (AC rate: 16.2%).
2018.12 - Our paper “Deep Recurrent Survival Analysis” has been selected as an oral presentation on Jan. 27.
Research
Data mining: sequence data mining and time-series data modeling.
Machine learning: meta-learning, AutoML and ensemble learning.
Reinforcement learning and its real-world applications.
Publications
Selected
SIMPLE: Specialized Model-Sample Matching for Domain Generalization, Z. Li, K. Ren (corresponding), et al.
Eleventh International Conference on Learning Representations (ICLR 2023).Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling, Y. Fang, K. Ren (corresponding), et al.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23).Towards Inference Efficient Deep Ensemble Learning, Z. Li, K. Ren (corresponding), et al.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23).Bootstrapped Transformer for Offline Reinforcement Learning, K. Wang, H. Zhao, X. Luo, K. Ren (corresponding), et al.
The 36th Conference on Neural Information Processing Systems (NeurIPS 2022).Reinforcement Learning with Automated Auxiliary Loss Search, T. He, Y. Zhang, K. Ren (corresponding), et al.
The 36th Conference on Neural Information Processing Systems (NeurIPS 2022).Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble, Z. Yang, K. Ren (corresponding), et al.
The 31st International Joint Conference on Artificial Intelligence (IJCAI-22).Towards Generating Real-World Time Series Data, H. Pei, K. Ren (corresponding), et al.
The 21th IEEE International Conference on Data Mining (ICDM).Universal Trading for Order Execution with Oracle Policy Distillation, Y. Fang, K. Ren (corresponding), et al.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling, J. Qin, K. Ren (corresponding), Y. Fang, W. Zhang and Y. Yu.
Proceedings of the 13th ACM International Conference on Web Search and Data Mining (WSDM 2020).Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction, K. Ren*, J. Qin*, Y. Fang*, W. Zhang, et al. (* = equal contribution)
Proceedings of 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019).Deep Recurrent Survival Analysis, K. Ren, J. Qin, L. Zheng, Z. Yang, W. Zhang, L. Qiu, Y. Yu
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising, K. Ren, W. Zhang, K. Chang, Y. Rong, Y. Yu and J. Wang
IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 4, pp. 645-659, April 1 2018.Real-Time Bidding by Reinforcement Learning in Display Advertising, H. Cai, K. Ren, W. Zhang, K. Malialis, J. Wang, Y. Yu, D. Guo
Proceedings of the Tenth ACM International Conference on Web Search and Data Mining Pages 661-670.Product-Based Neural Networks for User Response Prediction, Y. Qu, H. Cai, K. Ren, W. Zhang, Y. Yu, Y. Wen, J. Wang
2016 IEEE 16th International Conference on Data Mining (ICDM), Barcelona, 2016, pp. 1149-1154.
Thesis
Modeling and Decision Optimization in Real-time Bidding Advertising (Chinese) (PDF)
Topical
Chronological
Academic Service
PC member: ICML, NeurIPS, AAAI, SIGIR, etc.
Journal Reviewer: JMLR, TKDE, NeuralComputing, etc.
Education
Ph.D. in Computer Science, Shanghai Jiao Tong University, June 2019.
B.Eng. in Computer Science, Zhejiang University, June 2014.
Minor in Intensive Training Program of Innovation and Entrepreneurship, Chu Kochen Honors College at Zhejiang University, June 2014.
Exchange program to University of California Davis, Feburary 2012.