Publications

2026

  1. 2026
    How LLMs Are Persuaded: A Few Attention Heads, Rerouted
    Xiangkun Sun, Lingkai Kong, Aoqi Zhang, Liang Zeng, and Tonghan Wang
    arXiv preprint arXiv:2605.09314, 2026
    PDF
  2. 2026
    LLM Advertisement based on Neuron Auctions
    Peiran Yun, Wenxin Xu, Jiayuan Liu, Yihang Zhang, Liang Zeng, Lingkai Kong, and Tonghan Wang
    arXiv preprint arXiv:2605.08326, 2026
    PDF
  3. 2026
    Latent Spherical Flow Policy for Reinforcement Learning with Combinatorial Actions
    Lingkai Kong, Anagha Satish, Hezi Jiang, Akseli Kangaslahti, Andrew Ma, Wenbo Chen, Mingxiao Song, Lily Xu, and Milind Tambe
    In International Conference on Machine Learning (ICML) (Spotlight), 2026
    PDF
  4. 2026
    Reward Shaping for Inference-Time Alignment: A Stackelberg Game Perspective
    Haichuan Wang, Tao Lin, Lingkai Kong, Ce Li, Hezi Jiang, and Milind Tambe
    In International Conference on Machine Learning (ICML), 2026
    PDF
  5. 2026
    Policy-Embedded Graph Expansion: Networked HIV Testing with Diffusion-Driven Network Samples
    Akseli Kangaslahti, Davin Choo, Lingkai Kong, Milind Tambe, Alastair Heerden, and Cheryl Johnson
    In International Joint Conferences on Artificial Intelligence (IJCAI), 2026
    PDF
  6. 2026
    Diffusion-DFL: Decision-focused Diffusion Models for Stochastic Optimization
    Zihao Zhao, Christopher Yeh, Lingkai Kong, and Kai Wang
    In International Conference on Learning Representations (ICLR), 2026
    PDF
  7. 2026
    Generative Artificial Intelligence for Social Impact
    Lingkai Kong, Cheol Woo Kim, Davin Choo, and Milind Tambe
    IEEE Intelligent Systems, 2026
    DOI PDF
  8. 2026
    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
    In Innovative Applications of Artificial Intelligence (IAAI) (Oral), 2026
    PDF

2025

  1. 2025
    Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data
    Lingkai Kong, Haichuan Wang, Tonghan Wang, Guojun Xiong, and Milind Tambe
    In Advances in Neural Information Processing Systems (NeurIPS), 2025 (Spotlight)
    PDF
  2. 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, and Milind Tambe
    In Uncertainty in Artificial Intelligence (UAI), 2025
    PDF
  3. 2025
    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
  4. 2025
    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
  5. 2025
    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
  6. 2025
    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
  7. 2025
    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
  8. 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
    In International Conference on Learning Representations (ICLR), 2025
    PDF
  9. 2025
    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. 2024
    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 Advances in Neural Information Processing Systems (NeurIPS), 2024
    PDF
  2. 2024
    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 Advances in Neural Information Processing Systems (NeurIPS), 2024
    PDF
  3. 2024
    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. 2024
    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. 2024
    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. 2024
    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. 2023
    AdaPlanner: Adaptive Planning from Feedback with Language Models
    Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, and Chao Zhang
    In Advances in Neural Information Processing Systems (NeurIPS), 2023
    PDF
  2. 2023
    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. 2023
    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. 2023
    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. 2022
    End-to-End Stochastic Optimization with Energy-based Model
    Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, and Chao Zhang
    In Advances in Neural Information Processing Systems (NeurIPS), 2022 (Oral, 181/10411, top 1.76%)
    PDF
  2. 2022
    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. 2022
    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. 2021
    When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
    Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodríguez, Chao Zhang, and B. Aditya Prakash
    In Advances in Neural Information Processing Systems (NeurIPS), 2021
    PDF

2020

  1. 2020
    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. 2020
    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. 2018
    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