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cs.LG2025
Variation-Bounded Loss for Noise-Tolerant Learning
Jialiang Wang, Xiong Zhou, Xianming Liu +4
Mitigating the negative impact of noisy labels has been aperennial issue in supervised learning. Robust loss functions have emerged as a prevalent solution to this problem. In this…
cs.LG2024
Unraveling the Mechanics of Learning-Based Demonstration Selection for In-Context Learning
Hui Liu, Wenya Wang, Hao Sun +4
Large Language Models (LLMs) have demonstrated impressive in-context learning (ICL) capabilities from few-shot demonstration exemplars. While recent learning-based demonstration se…