Lingkai Kong
25 papers · 2020–2026 · 9 conferences · across top CS/AI conferences
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Conferences
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ICML (5)
ICLR (3)
UAI (3)
AAAI (2)
AISTATS (2)
COLT (1)
EMNLP (1)
NAACL (1)
Top co-authors
Keywords
large language model
(3)
uncertainty quantification
(3)
neural process
(2)
generative model
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lie group
(2)
variational inference
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sequential decision-making
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active learning
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graph classification
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text classification
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few-shot learning
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epistemic uncertainty
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data integration
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multimodal learning
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flow matching
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domain adaptation
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pseudo labeling
(1)
modality alignment
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time series
(1)
graph generation
(1)
Papers
Generative AI Against Poaching: Latent Composite Flow Matching for Poaching Prediction
AAAI 2026
Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning
ICML 2025
What is the Right Notion of Distance between Predict-then-Optimize Tasks?
UAI 2025
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints
AISTATS 2025
Robust Optimization with Diffusion Models for Green Security
UAI 2025
DF$^2$: Distribution-Free Decision-Focused Learning
UAI 2025
Trivialized Momentum Facilitates Diffusion Generative Modeling on Lie Groups
ICLR 2025
Efficient Evolutionary Search Over Chemical Space with Large Language Models
ICLR 2025
LLM-Augmented Chemical Synthesis and Design Decision Programs
ICML 2025
PRIORITY2REWARD: Incorporating Healthworker Preferences for Resource Allocation Planning
AAAI 2025
Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning
ICML 2024
Aligning Large Language Models with Representation Editing: A Control Perspective
NIPS 2024
Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis
NIPS 2024
Quantitative Convergences of Lie Group Momentum Optimizers
NIPS 2024
Convergence of Kinetic Langevin Monte Carlo on Lie groups
COLT 2024
Two Birds with One Stone: Enhancing Uncertainty Quantification and Interpretability with Graph Functional Neural Process
AISTATS 2024
Momentum Stiefel Optimizer, with Applications to Suitably-Orthogonal Attention, and Optimal Transport
ICLR 2023
AdaPlanner: Adaptive Planning from Feedback with Language Models
NIPS 2023
Autoregressive Diffusion Model for Graph Generation
ICML 2023
AcTune: Uncertainty-Based Active Self-Training for Active Fine-Tuning of Pretrained Language Models
NAACL 2022
End-to-end Stochastic Optimization with Energy-based Model
NIPS 2022
When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting
NIPS 2021
SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
ICML 2020
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function
NIPS 2020
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
EMNLP 2020