Agentic Intelligence Lab

Research

For the most up-to-date list of publications, please visit our Google Scholar profile. Below, we highlight our representative works in each active research direction. Following that, we provide a categorized list of our publications based on their type.

A word cloud of publication titles

All

Preprints and In-Submission Works:

  1. Wentse Chen, Jiayu Chen, Fahim Tajwar, Hao Zhu, Xintong Duan, Russ Salakhutdinov, and Jeff Schneider, “Retrospective In-Context Learning for Temporal Credit Assignment with Large Language Models”, submitted to International Conference on Machine Learning (ICML), 2025.

  2. Wentse Chen, Jiayu Chen, Shiyu Huang, and Jeff Schneider, “ME-IGM: Individual-Global-Max in Maximum Entropy Multi-Agent Reinforcement Learning”, submitted to International Conference on Machine Learning (ICML), 2025.

  3. Jiayu Chen, Wentse Chen, and Jeff Schneider, “Bayes Adaptive Monte Carlo Tree Search for Offline Model-based Reinforcement Learning”, submitted to International Conference on Machine Learning (ICML), 2025.

  4. Swetha Ganesh, Jiayu Chen, and Vaneet Aggarwal, “Improved Sample Complexity for Actor-Critic with Neural Critic Parametrization using Natural Policy Gradient and Data Drop”, submitted to International Conference on Machine Learning (ICML), 2025.

  5. Jiayu Chen, Bhargav Ganguly, Tian Lan, and Vaneet Aggarwal, “Variational Offline Multi-agent Skill Discovery”, submitted to International Joint Conferences on Artificial Intelligence (IJCAI), 2025.

  6. Jiayu Chen, Tian Lan, and Vaneet Aggarwal, “Hierarchical Deep Counterfactual Regret Minimization”, submitted to International Joint Conferences on Artificial Intelligence (IJCAI), 2025.

  7. Wenyan Xu, Jiayu Chen, Chen Li, Yonghong Hu, and Zhonghua Lu, “Mining Intraday Risk Factor Collections via Hierarchical Reinforcement Learning based on Transferred Options”, submitted to European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2025.

  8. Chang-Lin Chen, Jiayu Chen, Tian Lan, Elaine Zhao, Hongbo Dong, and Vaneet Aggarwal, “Rack Position Optimization in Large-Scale Heterogeneous Data Centers”, submitted to International Conference on Automated Planning and Scheduling (ICAPS), 2025.

  9. Bhargav Ganguly, Abhimanyu Shekhar, Chang-Lin Chen, Jiayu Chen, Vaneet Aggarwal, Shweta Singh, “A Deep Reinforcement Learning Approach for Circular Economy Management”, submitted to Nature Sustainability.

Workshop Papers:

  1. Wenyan Xu, Jiayu Chen, Chen Li, Yonghong Hu, and Zhonghua Lu, “Mining Intraday Risk Factor Collections via Hierarchical Reinforcement Learning based on Transferred Options”, accepted in AAAI 2025 Workshop on AI for Social Impact (Oral presentation).

  2. Chang-Lin Chen, Jiayu Chen, Tian Lan, Elaine Zhao, Hongbo Dong, and Vaneet Aggarwal, “Rack Position Optimization in Large-Scale Heterogeneous Data Centers”, accepted in AAAI/INFORMS Bridge: AI+ORMS, Dec 2024 (Full presentation).

  3. Wentse Chen, Jiayu Chen, Fahim Tajwar, Hao Zhu, Xintong Duan, Russ Salakhutdinov, and Jeff Schneider, “Fine-tuning LLM Agents with Retrospective In-Context Online Learning”, accepted in NeurIPS Adaptive Foundation Models Workshop, Oct 2024 (Oral presentation).

  4. Jiayu Chen, Marina Wagdy Wadea Haliem, Tian Lan, and Vaneet Aggarwal, “Multi-agent Deep Covering Option Discovery”, accepted in ICML Reinforcement Learning for Real Life Workshop, Jul 2021.

Conference Papers:

  1. Jiayu Chen, Vaneet Aggarwal, and Tian Lan, “A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process”, accepted in Conference on Neural Information Processing Systems (NeurIPS), Dec 2023.

  2. Jiayu Chen, Dipesh Tamboli, Tian Lan, and Vaneet Aggarwal, “Multi-task Hierarchical Adversarial Inverse Reinforcement Learning”, accepted in International Conference on Machine Learning (ICML), Jul 2023.

  3. Jiayu Chen, Tian Lan, and Vaneet Aggarwal, “Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control”, accepted in IEEE International Conference on Robotics and Automation (ICRA), Jun 2023.

  4. Jiayu Chen, Jingdi Chen, Tian Lan, and Vaneet Aggarwal, “Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs”, accepted in Conference on Neural Information Processing Systems (NeurIPS), Dec 2022.

  5. Jiayu Chen, Abhishek K. Umrawal, Tian Lan, and Vaneet Aggarwal, “DeepFreight: A Model-free Deep-reinforcement-learning-based Algorithm for Multi-transfer Freight Delivery”, accepted in International Conference on Automated Planning and Scheduling (ICAPS), Aug 2021.

  6. Pin Wang, Dapeng Liu, Jiayu Chen, Hanhan Li, and Ching-Yao Chan, “Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning”, accepted in IEEE International Conference on Robotics and Automation (ICRA), Jun 2021.

  7. Jilin Mei, Jiayu Chen, Wen Yao, Xijun Zhao, and Huijing Zhao, “Supervised Learning for Semantic Segmentation of 3D LiDAR Data”, accepted in IEEE Intelligent Vehicles Symposium (IV), Jun 2019.

Journal Papers:

  1. Swetha Ganesh, Jiayu Chen, Gugan Thoppe, and Vaneet Aggarwal, “Global Convergence Guarantees for Federated Policy Gradient Methods with Adversaries”, accepted in Transactions on Machine Learning Research (TMLR), Oct 2024.

  2. Chang-Lin Chen, Hanhan Zhou, Jiayu Chen, Mohammad Pedramfar, Vaneet Aggarwal, Tian Lan, Zheqing Zhu, Chi Zhou, Pol Mauri Ruiz, Neeraj Kumar, and Hongbo Dong, “Learning-based Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation”, accepted in IEEE Transactions on Network and Service Management (TNSM), Oct 2024.

  3. Jiayu Chen, Bhargav Ganguly, Yang Xu, Yongsheng Mei, Tian Lan, and Vaneet Aggarwal, “Deep Generative Models for Offline Policy Learning: Tutorial, Survey, and Perspectives on Future Directions”, accepted in Transactions on Machine Learning Research (TMLR), Aug 2024 (Survey Certification).

  4. Dipesh Tamboli, Jiayu Chen, Kiran Pranesh Jotheeswaran, Denny Yu, and Vaneet Aggarwal, “Reinforced Sequential Decision-Making for Sepsis Treatment: The PosNegDM Framework with Mortality Classifier and Transformer”, accepted in IEEE Journal of Biomedical and Health Informatics (JBHI), Mar 2024.

  5. Jiayu Chen, Vaneet Aggarwal, and Tian Lan, “Hierarchical Adversarial Inverse Reinforcement Learning”, accepted in IEEE Transactions on Neural Networks and Learning Systems (TNNLS), Aug 2023.

  6. Jiayu Chen, Jingdi Chen, Tian Lan, and Vaneet Aggarwal, “Learning Multi-agent Options for Tabular Reinforcement Learning using Factor Graphs”, accepted to IEEE Transactions on Artificial Intelligence (TAI), Jul 2022.