Long-Kai Huang
Email: HLONGKAI at gmail dot com
I am a senior researcher in Machine Learning 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.
I am generally interested in understanding the knowledge and the generalization mechanism in machine learning models and building general continual learning systems and focus on 1) Knowledge Transfer: continual learning, transfer learning, meta-learning; 2) Model Editing in Transformers; 3) Model Interpretability in Transformers and 4) their applications to real-world applications such as drug discovery.
Recent Publications | Full Publications (Google Scholar)
# indicates equal contributions. * indicates corresponding authors. Underlined authors are research interns mentored by me.
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
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)
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)
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
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
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
Concept-wise Fine-tuning Matters in Preventing Negative Transfer [paper]
Yunqiao Yang, Long-Kai Huang, Ying Wei
International Conference on Computer Vision (ICCV) 2023
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
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 (Oral)
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
Adversarial Task Up-sampling for Meta-learning [paper]
Yichen Wu, Long-Kai Huang*, Ying Wei*
Advances in Neural Information Processing Systems (NeurIPS) 2022
Frustratingly Easy Transferability Estimation [paper] [code]
Long-Kai Huang, Junzhou Huang, Yu Rong, Qiang Yang, Ying Wei
International Conference on Machine Learning (ICML), 2022
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
Communication-Efficient Distributed PCA by Riemannian Optimization [paper]
Long-Kai Huang and Sinno Jialin Pan
International Conference on Machine Learning (ICML), 2020