3 accepted papers
Zeroth-Order (ZO) optimization has emerged as a promising solution for fine-tuning LLMs under strict memory constraints, as it avoids the prohibitive memory cost of storing activations for backpropagation. However, existing ZO methods typically employ isotropic perturbations, neglecting the rich str…
Label noise model is a technique to construct controlled noisy datasets for evaluating noise-robust algorithms. However, the quality of the generated noise has not been evaluated thoroughly. In this paper, we propose a novel research question: Do the constructed datasets with the same noise rate hav…
Coreset selection is a technique that reduces model training overhead while retaining high accuracy by selecting a representative subset of the training data. The quality of the selected coresets can be assessed by quantifying the coverage of r-radius balls centered at each element to the entire dat…