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Publications (topical)
Reinforcement Learning
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, et al.
The 31st International Joint Conference on Artificial Intelligence (IJCAI-22).
Universal Trading for Order Execution with Oracle Policy Distillation, Y. Fang, K. Ren, et al.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21).
Interactive Recommender System via Knowledge Graph-enhanced Reinforcement Learning, S. Zhou, X. Dai, H. Chen, W. Zhang, K. Ren, et al.
Proceedings of 43nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020).
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.
Machine Learning
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).
Towards Generating Real-World Time Series Data, H. Pei, K. Ren, et al.
The 21th IEEE International Conference on Data Mining (ICDM).
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).
Guiding the One-to-one Mapping in CycleGAN via Optimal Transport, G. Lu, Z. Zhou, Y. Song, K. Ren, Y. Yu
Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19).
Activation Maximization Generative Adversarial Nets, Z. Zhou, H. Cai, S. Rong, Y. Song, K. Ren, J. Wang and Y. Yu
International Conference on Learning Representations 2018.
Information Retrieval and Recommendation
CoPE: Modeling Continuous Propagation and Evolution on Interaction Graph, Y. Zhang, Y. Xiong, D. Li, C. Shan, K. Ren and Y. Zhu.
Proceedings of the 2021 ACM on Conference on Information and Knowledge Management, ACM, 2021.
A Deep Recurrent Survival Model for Unbiased Ranking, J. Jin, Y. Fang, W. Zhang, K. Ren, et al.
Proceedings of 43nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020).
Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling, J. Qin, K. Ren, 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).
Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors’ Demonstration, X. Wang, L. Yu, K. Ren, G. Tao, W. Zhang, Y. Yu, and J. Wang
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2017, pp. 2051–2059.
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.
Computational Advertising
The Problems and Machine Learning Methods in Computational Advertising, W. Zhang, K. Ren, H. Zhang.
Communications of the CCF, vol. 5, May 1 2020.
Deep Landscape Forecasting for Real-time Bidding Advertising, K. Ren, J. Qin, L. Zheng, W. Zhang, Y. Yu.
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2019.
Learning Conversion Attribution with Dual-attention Mechanism for Online Advertising, K. Ren, Y. Fang, W. Zhang, S. Liu, J. Li, Y. Zhang, Y. Yu, J. Wang
Proceedings of the 2018 ACM on Conference on Information and Knowledge Management, ACM, 2018.
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.
Volume Ranking and Sequential Selection Programmatic Display Advertising, Y. Song, K. Ren, H. Cai, W. Zhang, Y. Yu
Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, ACM, 2017, pp. 1099–1107.
Managing Risk of Bidding Display Advertising, H. Zhang, W. Zhang, Y. Rong, K. Ren, W. Li, J. Wang
Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, ACM, 2017, pp. 581–590.
User Response Learning for Directly Optimizing Campaign Performance Display Advertising, K. Ren, W. Zhang, Y. Rong, H. Zhang, Y. Yu and J. Wang
Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. ACM, 2016: 679-688.
Functional Bid Landscape Forecasting for Display Advertising, Y. Wang, K. Ren, W. Zhang, J. Wang, Y. Yu
Joint European Conference on Machine Learning and Knowledge Discovery Databases, Springer, Cham, 2016, pp. 115–131.
Optimal Real-Time Bidding Frameworks Discussion, W. Zhang, K. Ren, J. Wang
arXiv Preprint.
Semantic Web and Natural Language Processing
A Graph Traversal Based Approach to Answer Non-Aggregation Questions over DBpedia, C. Zhu, K. Ren, X. Liu, H. Wang, Y. Tian, Y. Yu
Joint International Semantic Technology Conference, Springer, Cham, 2015, pp. 219–234.
Quantitative Finance
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