Publications (topical)

Reinforcement Learning

• Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance, Y. Fang, Z. Tang, K. Ren (corresponding), W. Liu, et al.
  Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2023.

• Is Risk-Sensitive Reinforcement Learning Properly Resolved?, R. Zhou, M. Liu, K. Ren (corresponding), et al.
  Preprint on arXiv.

• 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

• MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks, L. Zhang, Y. Zhang, K. Ren (corresponding), D. Li, Y. Yang
  The 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2024).

• CNN Kernels Can Be the Best Shapelets, Z. Qu, Y. Wang, X. Luo, W. He, K. Ren, D. Li
  The Twelfth International Conference on Learning Representations (ICLR 2024).

• Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling, K. Yi, Y. Wang, K. Ren (corresponding), D. Li.
  The 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

• ContiFormer: Continuous-Time Transformer for Irregular Time Series Modeling, Y. Chen, K. Ren (corresponding), Y. Wang, Y. Fang, W. Sun, D. Li.
  The 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

• 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.

AI for Healthcare

• Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling, K. Yi, Y. Wang, K. Ren (corresponding), D. Li.
  The 37th Conference on Neural Information Processing Systems (NeurIPS 2023).

• Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals, Y. Lan, K. Ren (corresponding), Y. Wang, W. Zheng, et al.
  Preprint on arXiv.

• Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling, Z. Li, Y. Fang, Y. Li, K. Ren (corresponding), et al.
  IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023.

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

• Learning Multi-Agent Intention-Aware Communication for Optimal Multi-Order Execution in Finance, Y. Fang, Z. Tang, K. Ren (corresponding), W. Liu, et al.
  Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2023.

• 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)