Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning
Cheol Woo Kim, Jai Moondra, Shresth Verma, Madeleine Pollack, Lingkai Kong, Milind Tambe, Swati Gupta
International Conference on Machine Learning (ICML), 2025
LLM-Augmented Chemical Synthesis and Design Decision Programs
Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du, Chao Zhang
International Conference on Machine Learning (ICML), 2025
Robust Optimization with Diffusion Models for Green Security
Lingkai Kong, Haichuan Wang, Yuqi Pan, Cheol Woo Kim, Mingxiao Song, Alayna Nguyen, Tonghan Wang, Haifeng Xu, Milind Tambe
Conference on Uncertainty in Artificial Intelligence (UAI), 2025
DF^2: Distribution-Free Decision-Focused Learning
Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B Aditya Prakash, Bo Dai and Chao Zhang
Conference on Uncertainty in Artificial Intelligence (UAI), 2025
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
Paula Rodriguez-Diaz, Lingkai Kong, Kai Wang, David Alvarez-Melis, Milind Tambe
Conference on Uncertainty in Artificial Intelligence (UAI), 2025
Efficient Evolutionary Search Over Chemical Space with Large Language Models
Haorui Wang, Marta Skreta, Cher-Tian Ser, Wenhao Gao, Lingkai Kong, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Yuanqi Du, Alán Aspuru-Guzik, Kirill Neklyudov and Chao Zhang
International Conference on Representation Learning (ICLR), 2025
[Paper]
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
Lingkai Kong*, Yuanqi Du*, Wenhao Mu*, Kirill Neklyudov, Valentin De Bortol, Dongxia Wu, Haorui Wang, Aaron Ferber, Yi-An Ma, Carla P. Gomes, Chao Zhang
International Conference on Artificial Intelligence and Statistics (AISTATS), 2025
[Paper]
Aligning Large Language Models with Representation Editing: A Control Perspective
Lingkai Kong*, Haorui Wang*, Wenhao Mu*, Yuanqi Du, Yuchen Zhuang, Yifei Zhou, Yue Song, Rongzhi Zhang, Kai Wang, Chao Zhang
Advances in Neural Information Processing Systems (NeurIPS), 2024
[Paper]
Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis
Haoxin Liu, Shangqing Xu, Zhiyuan Zhao, Lingkai Kong, Harshavardhan Kamarthi, Aditya B Sasanur, Megha Sharma, Jiaming Cui, Qingsong Wen, Chao Zhang and B Aditya Prakash
Advances in Neural Information Processing Systems (NeurIPS), 2024
[Paper]
TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
Haorui Wang, Rongzhi Zhang, Yinghao Li, Lingkai Kong, Yuchen Zhuang, Xiusi Chen, Chao Zhang
Conference On Language Modeling (COLM), 2024
[Paper]
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang and B. Aditya Prakash
International Conference on Machine Learning (ICML), 2024
[Paper]
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
Lingkai Kong*, Haotian Sun*, Yuchen Zhuang, Haorui Wang, Wenhao Mu and Chao Zhang
International Conference on Artificial Intelligence and Statistics (AISTATS), 2024
[Paper]
AdaPlanner: Adaptive Planning from Feedback with Language Models
Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai and Chao Zhang
Advances in Neural Information Processing Systems (NeurIPS), 2023
[Paper]
MUBen: Benchmarking the Uncertainty of Pre-Trained Models for Molecular Property Prediction
Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu and Chao Zhang
Transactions on Machine Learning Research (TMLR), NeurIPS Workshop on AI4Science, 2023
[Paper]
Uncertainty Quantification in Deep Learning
Lingkai Kong, Harshavardhan Kamarthi, Peng Chen, B Aditya Prakash and Chao Zhang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023 (Conference Tutorial)
[Tutorial page]
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang and B Aditya Prakash
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023
[Paper]
DyGen: Fine-Tuning Language Models with Noisy Labels by Dynamics-Enhanced Generative Modeling
Yuchen Zhuang, Yue Yu, Lingkai Kong, Xiang Chen and Chao Zhang
ACM SIGKDD Conference on Knowledge Discovery and Data Mining (SIGKDD), 2023
[Paper]
Autoregressive Diffusion Model for Graph Generation
Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B Aditya Prakash and Chao Zhang
International Conference on Machine Learning (ICML), 2023
[Paper]
End-to-End Stochastic Optimization with Energy-based Model
Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B Aditya Prakash and Chao Zhang
Advances in Neural Information Processing Systems (NeurIPS), 2022 (Selected as Oral)
[Paper]
[Code]
AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models
Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, Chao Zhang
Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022
[Paper]
[Code]
CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang and B Aditya Prakash
The Web Conference (WWW), 2022
[Paper]
[Code]
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang and B Aditya Prakash
Advances in Neural Information Processing Systems (NeurIPS), 2021
[Paper]
[Code]
