Kong, Lingkai

33 publications

NeurIPS 2025 Composite Flow Matching for Reinforcement Learning with Shifted-Dynamics Data Lingkai Kong, Haichuan Wang, Tonghan Wang, Guojun Xiong, Milind Tambe

UAI 2025 DF$^2$: Distribution-Free Decision-Focused Learning Lingkai Kong, Wenhao Mu, Jiaming Cui, Yuchen Zhuang, B. Aditya Prakash, Bo Dai, Chao Zhang

AISTATS 2025 Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints Lingkai Kong, Yuanqi Du, Wenhao Mu, Kirill Neklyudov, Valentin De Bortoli, Dongxia Wu, Haorui Wang, Aaron M Ferber, Yian Ma, Carla P Gomes, Chao Zhang

ICLR 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, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

ICML 2025 LLM-Augmented Chemical Synthesis and Design Decision Programs Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du, Chao Zhang

ICLRW 2025 LLM-Augmented Chemical Synthesis and Design Decision Programs Haorui Wang, Jeff Guo, Lingkai Kong, Rampi Ramprasad, Philippe Schwaller, Yuanqi Du, Chao Zhang

ICML 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, Swati Gupta

AAAI 2025 PRIORITY2REWARD: Incorporating Healthworker Preferences for Resource Allocation Planning Shresth Verma, Alayna Nguyen, Niclas Boehmer, Lingkai Kong, Milind Tambe

UAI 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

ICLR 2025 Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups Yuchen Zhu, Tianrong Chen, Lingkai Kong, Evangelos Theodorou, Molei Tao

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

NeurIPS 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, Chao Zhang

ICMLW 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, Chao Zhang

NeurIPSW 2024 Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards Shresth Verma, Niclas Boehmer, Lingkai Kong, Milind Tambe

COLT 2024 Convergence of Kinetic Langevin Monte Carlo on Lie Groups Lingkai Kong, Molei Tao

ICMLW 2024 Efficient Evolutionary Search over Chemical Space with Large Language Models Haorui Wang, Marta Skreta, Yuanqi Du, Wenhao Gao, Lingkai Kong, Cher Tian Ser, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

ICMLW 2024 Efficient Evolutionary Search over Chemical Space with Large Language Models Haorui Wang, Marta Skreta, Yuanqi Du, Wenhao Gao, Lingkai Kong, Cher Tian Ser, Felix Strieth-Kalthoff, Chenru Duan, Yuchen Zhuang, Yue Yu, Yanqiao Zhu, Alan Aspuru-Guzik, Kirill Neklyudov, Chao Zhang

TMLR 2024 MUBen: Benchmarking the Uncertainty of Molecular Representation Models Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang

NeurIPSW 2024 Prioritization Strategies for LLM-Designed Restless Bandit Rewards in Public Health Shresth Verma, Niclas Boehmer, Lingkai Kong, Milind Tambe

NeurIPS 2024 Quantitative Convergences of Lie Group Momentum Optimizers Lingkai Kong, Molei Tao

NeurIPS 2024 Time-MMD: 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, B. Aditya Prakash

ICML 2024 Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning Haoxin Liu, Harshavardhan Kamarthi, Lingkai Kong, Zhiyuan Zhao, Chao Zhang, B. Aditya Prakash

AISTATS 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, Chao Zhang

NeurIPS 2023 AdaPlanner: Adaptive Planning from Feedback with Language Models Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang

NeurIPSW 2023 AdaPlanner: Adaptive Planning from Feedback with Language Models Haotian Sun, Yuchen Zhuang, Lingkai Kong, Bo Dai, Chao Zhang

ICML 2023 Autoregressive Diffusion Model for Graph Generation Lingkai Kong, Jiaming Cui, Haotian Sun, Yuchen Zhuang, B. Aditya Prakash, Chao Zhang

NeurIPSW 2023 MUBen: Benchmarking the Uncertainty of Molecular Representation Models Yinghao Li, Lingkai Kong, Yuanqi Du, Yue Yu, Yuchen Zhuang, Wenhao Mu, Chao Zhang

ICLR 2023 Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport Lingkai Kong, Yuqing Wang, Molei Tao

NeurIPS 2022 End-to-End Stochastic Optimization with Energy-Based Model Lingkai Kong, Jiaming Cui, Yuchen Zhuang, Rui Feng, B. Aditya Prakash, Chao Zhang

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

ICML 2020 SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates Lingkai Kong, Jimeng Sun, Chao Zhang

NeurIPS 2020 Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function Lingkai Kong, Molei Tao

UAI 2018 Learning Deep Hidden Nonlinear Dynamics from Aggregate Data Yisen Wang, Bo Dai, Lingkai Kong, Sarah Monazam Erfani, James Bailey, Hongyuan Zha