Publications

2025

  1. Preprints
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    Generative AI Against Poaching: Latent Composite Flow Matching for Wildlife Conservation
    Lingkai Kong, Haichuan Wang, Charles A. Emogor, Vincent Börsch-Supan, Lily Xu, and Milind Tambe
    2025
    Preprints
    PDF
  2. Preprints
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    Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
    Lingkai Kong, Haichuan Wang, Tonghan Wang, Guojun Xiong, and Milind Tambe
    2025
    Preprints
    PDF
  3. UAI
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    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, and Milind Tambe
    In Uncertainty in Artificial Intelligence (UAI), 2025
    PDF
  4. UAI
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    What is the Right Notion of Distance between Predict-then-Optimize Tasks?
    Paula Rodriguez-Diaz, Lingkai Kong, Kai Wang, David Alvarez-Melis, and Milind Tambe
    In Uncertainty in Artificial Intelligence (UAI), 2025
    PDF
  5. UAI
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    DF^2: Distribution-Free Decision-Focused Learning
    Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, and Chao Zhang
    In Uncertainty in Artificial Intelligence (UAI), 2025
    PDF
  6. AAAI
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    PRIORITY2REWARD: Incorporating Healthworker Preferences for Resource Allocation Planning
    Shresth Verma, Alayna Nguyen, Niclas Boehmer, Lingkai Kong, and Milind Tambe
    In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
    PDF
  7. AAMAS-AASG
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    LLM-based Agent Simulation for Maternal Health Interventions: Uncertainty Estimation and Decision-focused Evaluation
    Sarah Martinson, Lingkai Kong, Cheol Woo Kim, Aparna Taneja, and Milind Tambe
    In AAMAS workshop on Autonomous Agents for Social Good (AASG), 2025
    PDF
  8. ICML
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    Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning
    Cheol Woo Kim, Jai Moondra, Shresth Verma, Madeleine Pollack, Lingkai Kong, Milind Tambe, and Swati Gupta
    In International Conference on Machine Learning (ICML), 2025
    PDF
  9. ICML
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    LLM-Augmented Chemical Synthesis and Design Decision Programs
    Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du, and Chao Zhang
    In International Conference on Machine Learning (ICML), 2025
    PDF
  10. ICLR
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    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
    In International Conference on Learning Representations (ICLR), 2025
    PDF
  11. AISTATS
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    Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
    Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortol, Haorui Wang, Dongxia Wu, Aaron Ferber, Yi-An Ma, Carla P. Gomes, and Chao Zhang
    In Artificial Intelligence and Statistics (AISTATS), 2025
    PDF

2024

  1. NeurIPS
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    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, and Chao Zhang
    In NeurIPS, 2024
    PDF
  2. NeurIPS
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    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
    In NeurIPS, 2024
    PDF
  3. COLM
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    TPD: Enhancing Student Language Model Reasoning via Principle Discovery and Guidance
    Haorui Wang, Rongzhi Zhang, Yinghao Li, Lingkai Kong, Yuchen Zhuang, Xiusi Chen, and Chao Zhang
    In Conference On Language Modeling (COLM), 2024
    PDF
  4. ICML
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    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
    In International Conference on Machine Learning (ICML), 2024
    PDF
  5. AISTATS
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    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
    In Artificial Intelligence and Statistics (AISTATS), 2024
    PDF
  6. TMLR
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    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), 2024
    PDF

2023

  1. NeurIPS
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    AdaPlanner: Adaptive Planning from Feedback with Language Models
    Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang
    In NeurIPS, 2023
    PDF
  2. KDD
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    When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting
    Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, and B. Aditya Prakash
    In Knowledge Discovery and Data Mining (KDD), 2023
    PDF
  3. KDD
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    DyGen: Fine-Tuning Language Models with Noisy Labels by Dynamics-Enhanced Generative Modeling
    Yuchen Zhuang, Yue Yu, Lingkai Kong, Xiang Chen, and Chao Zhang
    In Knowledge Discovery and Data Mining (KDD), 2023
    PDF
  4. ICML
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    Autoregressive Diffusion Model for Graph Generation
    Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, and Chao Zhang
    In International Conference on Machine Learning (ICML), 2023
    PDF

2022

  1. NeurIPS
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    End-to-End Stochastic Optimization with Energy-based Model
    Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, and Chao Zhang
    In NeurIPS, (Oral, 184/10411, top 1.76%), 2022
    PDF
  2. NAACL
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    AcTune: Uncertainty-Aware Active Self-Training for Active Fine-Tuning of Pretrained Language Models
    Yue Yu, Lingkai Kong, Jieyu Zhang, Rongzhi Zhang, and Chao Zhang
    In North American Chapter of the Association for Computational Linguistics (NAACL), 2022
    PDF
  3. WWW
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    CAMul: Calibrated and Accurate Multi-view Time-Series Forecasting
    Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, and B. Aditya Prakash
    In The Web Conference (WWW), 2022
    PDF

2021

  1. NeurIPS
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    When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
    Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, and B. Aditya Prakash
    In NeurIPS, 2021
    PDF

2020

  1. EMNLP
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    Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
    Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lye, Tuo Zhao, and Chao Zhang
    In Empirical Methods in Natural Language Processing (EMNLP), 2020
    PDF
  2. ICML
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    SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
    Lingkai Kong, Jimeng Sun, and Chao Zhang
    In International Conference on Machine Learning (ICML), 2020
    PDF

2018

  1. UAI
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    Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
    Yisen Wang, Bo Dai, Lingkai Kong, and Hongyuan Zha
    In Uncertainty in Artificial Intelligence (UAI), 2018
    PDF