3 papers
cs.LG2026
Model Merging as Probabilistic Inference in Fine-Tuning Parameter Space
Long Minh Bui, Tuan Anh Le Van, Tung Phi Duc +3
Model merging aims to combine existing single-task solutions into a multi-task solution without additional data-driven fine-tuning.~Most existing approaches achieve this using geom…
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
Revisiting Kernel Attention with Correlated Gaussian Process Representation
Long Minh Bui, Tho Tran Huu, Duy Dinh +2
Transformers have increasingly become the de facto method to model sequential data with state-of-the-art performance. Due to its widespread use, being able to estimate and calibrat…