1 citations · 1 across the 5 of their papers we have counts for
6 papers
Dynamic Shapley Computation
Xuan Yang, Hsi-Wen Chen, Ming-Syan Chen +1
Shapley-based data valuation provides a principled way to quantify the contribution of training data, but its high computational cost makes it impractical in dynamic settings where…
Interference-Aware Multi-Task Unlearning
Ying-Hua Huang, Rui Fang, Hsi-Wen Chen +1
Machine unlearning aims to remove the contribution of designated training data from a trained model while preserving performance on the remaining data. Existing work mainly focuses…
LoopQ: Quantization for Looped Language Models
Rui Fang, Hsi-Wen Chen, Ming-Syan Chen +1
Looped language models (LoopLMs) increase effective computational depth by repeatedly applying shared Transformer blocks without increasing the parameter count. However, this reuse…
Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation
Xuan Yang, Hsi-Wen Chen, Ming-Syan Chen +1
The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global…
Dual Adversarial Alignment for Realistic Support-Query Shift Few-shot Learning
Siyang Jiang, Rui Fang, Hsi-Wen Chen +2
Support-query shift few-shot learning aims to classify unseen examples (query set) to labeled data (support set) based on the learned embedding in a low-dimensional space under a d…
PGADA: Perturbation-Guided Adversarial Alignment for Few-shot Learning Under the Support-Query Shift
Siyang Jiang, Wei Ding, Hsi-Wen Chen +1
Few-shot learning methods aim to embed the data to a low-dimensional embedding space and then classify the unseen query data to the seen support set. While these works assume that…