3 papers
cs.LG2026
Continual Fine-Tuning with Provably Accurate and Parameter-Free Task Retrieval
Hang Thi-Thuy Le, Long Minh Bui, Minh Hoang +1
Continual fine-tuning aims to adapt a pre-trained backbone to new tasks sequentially while preserving performance on earlier tasks whose data are no longer available. Existing appr…
cs.LG2025
Probabilistic Federated Prompt-Tuning with Non-IID and Imbalanced Data
Pei-Yau Weng, Minh Hoang, Lam M. Nguyen +3
Fine-tuning pre-trained models is a popular approach in machine learning for solving complex tasks with moderate data. However, fine-tuning the entire pre-trained model is ineffect…
cs.LG2025
Learning Surrogates for Offline Black-Box Optimization via Gradient Matching
Minh Hoang, Azza Fadhel, Aryan Deshwal +2
Offline design optimization problem arises in numerous science and engineering applications including material and chemical design, where expensive online experimentation necessita…