Long-Kai Huang
Email: HLONGKAI at gmail dot com
I am a senior researcher in AI for Science Center, Tencent AI Lab. Prior to that, I obtained my Ph.D. in SCSE at NTU, advised by Prof. Sinno Jialin Pan. I received B.Eng. from Sun Yat-Sen University and did my undergraduate thesis with Prof. Wei-Shi Zheng.
My research focuses on foundational theory and applications of meta-learning, continual learning, and efficient pre-training and fine-tuning of LLMs. I am also interested in their applications to AI for science, particularly in drug discovery and protein design.
Recent Publications | Full Publications (Google Scholar)
# indicates corresponding authors. * indicates equal contributions. Underlined authors are research interns mentored by me.
Steering Protein Language Models
Long-Kai Huang#, Rongyi Zhu, Bing He, Jianhua Yao
International Conference on Machine Learning (ICML), 2025
[Model Editing][AI4Sci]
IBCircuit: Towards Holistic Circuit Discovery with Information Bottleneck
Tian Bian, Yifan Niu, Chaohao Yuan, Chengzhi Piao, Bingzhe Wu, Long-Kai Huang, Yu Rong, Tingyang Xu, Hong Cheng, Jia Li
International Conference on Machine Learning (ICML), 2025
[LLMs][Explainable AI]
SD-LoRA: Scalable Decoupled Low-Rank Adaptation for Class Incremental Learning
Yichen Wu*, Hongming Piao*, , Long-Kai Huang#, Renzhen Wang, Wanhua Li, Hanspeter Pfister, Deyu Meng,
Kede Ma#, Ying Wei#
International Conference on Learning Representations (ICLR) 2025 (Oral)
[Continual Learning]
Parameter and Memory Efficient Pretraining via Low-rank Riemannian Optimization
Zhanfeng Mo, Long-Kai Huang, Sinno Jialin Pan
International Conference on Learning Representations (ICLR) 2025
[LLMs][Efficient Learning]
Atomas: Hierarchical Adaptive Alignment on Molecule-Text for Unified Molecule Understanding and Gen-
eration
Yikun Zhang, Geyan Ye, Chaohao Yuan, Bo Han, Long-Kai Huang, Jianhua Yao, Wei Liu, Yu Rong
International Conference on Learning Representations (ICLR) 2025
[AI4Sci]
Learning Where to Edit Vision Transformers
Yunqiao Yang, Long-Kai Huang#, Shengzhuang Chen, Kede Ma, Ying Wei#
Advances in Neural Information Processing Systems (NeurIPS) 2024
[Model Editing][Meta Learning]
Towards Understanding Evolving Patterns in Sequential Data
Qiuhao Zeng, Long-Kai Huang, Xi Chen, Charles Ling, Boyu Wang
Advances in Neural Information Processing Systems (NeurIPS) 2024 (Spotlight)
[Sequential Learning]
Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations Via Pareto Optimization
Yichen Wu*, Hong Wang*, Peilin Zhao, Yefeng Zheng, Ying Wei#, Long-Kai Huang#
International Conference on Machine Learning (ICML), 2024
[Continual Learning][Optimization Theory]
Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction [paper]
Yichen Wu, Long-Kai Huang#, Renzhen Wang, Deyu Meng, Ying Wei#
International Conference on Learning Representations (ICLR) 2024 (Oral, Outstanding Paper Award Honorable Mention)
[Continual Learning][Meta Learning][Optimization Theory]
Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time [paper]
Qiuhao Zeng, Changjian Shui, Long-Kai Huang, Peng Liu, Xi Chen, Charles Ling, Boyu Wang
International Conference on Learning Representations (ICLR) 2024 (Oral)
[Sequential Learning]
Deep domain adversarial neural network for the deconvolution of cell type mixtures in tissue proteome profiling
Fang Wang*, Fan Yang*, Long-Kai Huang, Wei Li, Jiangning Song, Robin B Gasser, Ruedi Aebersold, Guohua Wang, Jianhua Yao
Nature Machine Intelligence 2023
[Transfer Learning][AI4Sci]
Retaining Beneficial Information from Detrimental Data for Neural Network Repair [paper]
Long-Kai Huang, Peilin Zhao, Junzhou Huang, Sinno Jialin Pan
Advances in Neural Information Processing Systems (NeurIPS) 2023
[Model Editing][Transfer Learning]
Secure Out-of-Distribution Task Generalization with Energy-Based Models [paper]
Shengzhuang Chen, Long-Kai Huang, Jonathan Richard Schwarz, Yilun Du, Ying Wei
Advances in Neural Information Processing Systems (NeurIPS) 2023
[Meta Learning]
Concept-wise Fine-tuning Matters in Preventing Negative Transfer [paper]
Yunqiao Yang, Long-Kai Huang, Ying Wei
International Conference on Computer Vision (ICCV) 2023
[Transfer Learning]
Improving Generalizability of Graph Anomaly Detection Models via Data Augmentation
Shuang Zhou, Xiao Huang, Ninghao Liu, Huachi Zhou, Fu-Lai Chung, Long-Kai Huang#
IEEE Transactions on Knowledge and Data Engineering (TKDE) 2023
[Meta Learning]
DrugOOD: Out-of-Distribution Dataset Curator and Benchmark for AI-Aided Drug Discovery–a Focus on Affinity Prediction Problems with Noise Annotations
Yuanfeng Ji, Lu Zhang [and 15 others, including Long-Kai Huang]
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) 2023
[AI4Sci]
Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization [paper] [code]
Long-Kai Huang, Ying Wei
Advances in Neural Information Processing Systems (NeurIPS) 2022
[Meta Learning]
Adversarial Task Up-sampling for Meta-learning [paper]
Yichen Wu, Long-Kai Huang#, Ying Wei#
Advances in Neural Information Processing Systems (NeurIPS) 2022
[Meta Learning]
Frustratingly Easy Transferability Estimation [paper] [code]
Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, Ying Wei
International Conference on Machine Learning (ICML), 2022
[Transfer Learning]
Improving Generalization in Meta-learning via Task Augmentation [paper] [code]
Huaxiu Yao*, Long-Kai Huang*, Linjun Zhang, Ying Wei, Li Tian, James Zou, Junzhou Huang, Zhenhui Li
International Conference on Machine Learning (ICML), 2021
[Meta Learning][AI4Sci][Optimization Theory]
Communication-Efficient Distributed PCA by Riemannian Optimization [paper]
Long-Kai Huang and Sinno Jialin Pan
International Conference on Machine Learning (ICML), 2020
[Optimization Theory]
Accelerate Learning of Deep Hashing With Gradient Attention
Long-Kai Huang, Jianda Chen, Sinno Jialin Pan
International Conference on Computer Vision (ICCV), 2019
[Meta Learning]