Full Publication

My research interest includes black-box optimizaiton algorithms: Evolutionary Algorithms, such as quality-diversity optimization, cooperative coevolution, evolutionary gradient algorithm. Learning to Optimize, such as Bayesian optimization, learning to evaluate, learning to configurate, learning the search space, and learning to transfer. and their applications: Electronic Design Automation, such as macro placement and global placement. AI for Science. Human-AI Coordination. # Equal Contribution

Journal Article

PNAS   Reducing the Uncertainty in Estimating Soil Microbial Derived Carbon Storage. Han Hu#, Chao Qian#, Ke Xue#, Rainer Georg Jörgensen, Marco Keiluweit, Chao Liang, Xuefeng Zhu, Ji Chen, Yishen Sun, Haowei Ni, Jixian Ding, Weigen Huang, Jingdong Mao, Rong-Xi Tan, Jizhong Zhou, Thomas W. Crowther, Zhi-Hua Zhou, Jiabao Zhang, and Yuting Liang. Proceedings of the National Academy of Sciences, 2024, 121(35): e2401916121. PDF / Code TL;DR: We use AI techniques for estimating soil microbial derived carbon storage.

IEEE TEvC   Heterogeneous Multi-Agent Zero-Shot Coordination by Coevolution. Ke Xue, Yutong Wang, Cong Guan, Lei Yuan, Haobo Fu, Qiang Fu, Chao Qian, and Yang Yu. IEEE Transactions on Evolutionary Computation. PDF / Code TL;DR: We provide an effective framework to solve heterogeneous human-AI coordination.

TMLR   One by One, Continual Coordinating with Humans via Hyper-Teammate Identification. Cong Guan, Feng Chen, Ke Xue, Chunpeng Fan, Lichao Zhang, Ziqian Zhang, Pengyao Zhao, Zongzhang Zhang, Chao Qian, Lei Yuan, Yang Yu. Transactions on Machine Learning Research.

FCS   Open and Real-World Human-AI Coordination by Heterogeneous Training with Communication. Cong Guan, Ke Xue, Chunpeng Fan, Feng Chen, Lei Yuan, Chao Qian, and Yang Yu. Frontiers of Computer Science, 2025, 19(4): 194314. PDF

Conference Paper

DAC   ReMaP: Macro Placement by Recursively Prototyping and Periphery-Guided Relocating. Yunqi Shi, Xi Lin, Siyuan Xu, Shixiong Kai, Ke Xue, Mingxuan Yuan, Chao Qian and Zhi-Hua Zhou In: Proceedings of the 62nd ACM/IEEE Design Automation Conference (DAC’25), San Francisco, CA, 2025.

ICLR   Offline Model-Based Optimization by Learning to Rank. Rong-Xi Tan, Ke Xue, Shen-Huan Lyu, Haopu Shang, Yao Wang, Yaoyuan Wang, Sheng Fu, and Chao Qian. In: Proceedings of the 13th International Conference on Learning Representation (ICLR’25), Singapore, 2025. PDF / Code

AAAI   Oral   Pareto Set Learning for Multi-Objective Reinforcement Learning. Erlong Liu, Yu-Chang Wu, Xiaobin Huang, Chengrui Gao, Ren-Jian Wang, Ke Xue, and Chao Qian. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), Philadelphia, PA, 2025.

DATE   Best Paper Award   Timing-Driven Global Placement by Efficient Critical Path Extraction. Yunqi Shi, Siyuan Xu, Shixiong Kai, Xi Lin, Ke Xue, Mingxuan Yuan, and Chao Qian. In: Proceedings of 2025 Design, Automation & Test in Europe Conference & Exhibition (DATE'25), Lyon, France, 2025. TL;DR: We provide an effective timing-driven global placement algorithm.

NeurIPS   Reinforcement Learning Policy as Macro Regulator Rather than Macro Placer. Ke Xue, Ruo-Tong Chen, Xi Lin, Yunqi Shi, Shixiong Kai, Siyuan Xu, and Chao Qian. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024. PDF / Code TL;DR: We provide a new paradigm of reinforcement learning for macro placement.

NeurIPS   Spotlight   Monte Carlo Tree Search based Space Transfer for Black Box Optimization. Shukuan Wang#, Ke Xue#, Lei Song, Xiaobin Huang, and Chao Qian. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024. PDF / Code TL;DR: We can transfer information from diverse source tasks to help the optimization of target task.

ICML   Offline Multi-Objective Optimization. Ke Xue#, Rong-Xi Tan#, Xiaobin Huang, and Chao Qian. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024. PDF / Code TL;DR: We formulate and provide the first benchmark for offline multi-objective optimization.

ICML   Quality-Diversity with Limited Resources. Ren-Jian Wang, Ke Xue, Cong Guan, and Chao Qian. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024, pp.51984-52001. PDF / Code

IJCAI   Quality-Diversity Algorithms Can Provably Be Helpful for Optimization. Chao Qian, Ke Xue, and Ren-Jian Wang. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju Island, Korea, 2024, pp.6994-7002. PDF TL;DR: We give the first theoretical explanation of the superior optimization ability of quality-diversity algorithms.

IJCAI   Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy. Chengrui Gao, Haopu Shang, Ke Xue, Dong Li, and Chao Qian. In: Proceedings of the 33rd International Joint Conference on Artificial Intelligence (IJCAI'24), Jeju Island, Korea, 2024, pp.6914-6922. PDF / Code

DAC   Escaping Local Optima in Global Placement. Ke Xue#, Xi Lin#, Yunqi Shi, Shixiong Kai, Siyuan Xu, and Chao Qian. In: Proceedings of the 61st ACM/IEEE Design Automation Conference (DAC'24), San Francisco, CA, 2024. (Work-in-Progress poster) PDF

ICLR   Spotlight   Sample-Efficient Quality-Diversity by Cooperative Coevolution. Ke Xue# , Ren-Jian Wang#, Pengyi Li, Dong Li, Jianye Hao, and Chao Qian. In: Proceedings of the 12th International Conference on Learning Representations (ICLR'24), Vienna, Austria, 2024. PDF / Code TL;DR: We decompose and coevolve solutions to improve the sample efficieny of QD algorithms.

AAAI   Stochastic Bayesian Optimization with Unknown Continuous Context Distribution via Kernel Density Estimation. Xiaobin Huang, Lei Song, Ke Xue, and Chao Qian. In: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI'24), Vancouver, Canada, 2024, pp.12635-12643. PDF / Code

NeurIPS   21st ACM SIGEVO Humies Bronze Award   Macro Placement by Wire-Mask-Guided Black-Box Optimization. Yunqi Shi, Ke Xue, Lei Song, and Chao Qian. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), New Orleans, LA, 2023. PDF / Code TL;DR: We provide a new paradigm of black-box optimization for chip placement.

IJCAI   Multi-objective Optimization-based Selection for Quality-Diversity by Non-surrounded-dominated Sorting. Ren-Jian Wang, Ke Xue, Haopu Shang, Chao Qian, Haobo Fu, and Qiang Fu. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macao, SAR, China, 2023, pp.4335-4343. PDF / Code

UAI   Fast teammate adaptation in the presence of sudden policy change. Ziqian Zhang, Lei Yuan, Lihe Li, Ke Xue, Chengxing Jia, Cong Guan, Chao Qian, and Yang Yu. In: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI'23), Pittsburgh, PA, 2023, pp.2465-2476. PDF

AAAI   Oral   Robust multi-agent coordination via evolutionary generation of auxiliary adversarial attackers. Lei Yuan, Zi-Qian Zhang, Ke Xue, Hao Yin, Feng Chen, Cong Guan, Li-He Li, Chao Qian, and Yang Yu. . In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), Washington, DC, 2023, pp.11753-11762. PDF / Code

NeurIPS   Spotlight   Multi-Agent Dynamic Algorithm Configuration. Ke Xue#, Jiacheng Xu#, Lei Yuan, Miqing Li, Chao Qian, Zongzhang Zhang, and Yang Yu. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), New Orleans, LA, 2022. PDF / Code TL;DR: We can effectively adjust all the hyperparameters of the optimization algorithm online.

NeurIPS   Spotlight   Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization. Lei Song#, Ke Xue#, Xiaobin Huang, and Chao Qian. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), New Orleans, LA, 2022. PDF / Code TL;DR: We can adaptively select effective variables for high-dimensional optimization.

ICLR   Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning. Yutong Wang#, Ke Xue#, and Chao Qian. In: Proceedings of the 10th International Conference on Learning Representations (ICLR'22), Virtual, 2022. PDF / Code

IJCAI   Evolutionary Gradient Descent for Non-convex Optimization. Ke Xue, Chao Qian, Ling Xu, and Xu-Dong Fei. In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Virtual, 2021. PDF / Code TL;DR: We provide an evolutionary gradient algorithm with theoretical guarentee.

IJCAI   Bayesian Optimization using Pseudo-Points. Chao Qian, Hang Xiong, and Ke Xue. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), Yokohama, Japan, 2020, pp.3044-3050. PDF

Manuscripts

arXiv   Reinforced In-Context Black-Box Optimization. Lei Song, Chenxiao Gao, Ke Xue, Chenyang Wu, Dong Li, Jianye Hao, Zongzhang Zhang, and Chao Qian. arXiv, 2024. PDF / Code

NeurIPS Workshop   Diversity from Human Feedback. Ren-Jian Wang#, Ke Xue#, Yutong Wang, Peng Yang, Haobo Fu, Qiang Fu, and Chao Qian. In: 2nd Agent Learning in Open-Endedness Workshop at NeurIPS'23, New Orleans, LA, 2023. PDF