LiLAW learns to weight samples as easy, moderate or hard using three global scalars updated by one gradient step on a validation batch to improve noisy training performance.
Selective classification via neural network training dynamics
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2025 2verdicts
UNVERDICTED 2representative citing papers
The paper introduces clean-model-based metrics that stratify test samples by vulnerability to targeted poisoning, enabling worst-case attack evaluation and vulnerability-aware defenses.
citing papers explorer
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LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Training
LiLAW learns to weight samples as easy, moderate or hard using three global scalars updated by one gradient step on a validation batch to improve noisy training performance.
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Are Targeted Data Poisoning Attacks as Effective as We Think?
The paper introduces clean-model-based metrics that stratify test samples by vulnerability to targeted poisoning, enabling worst-case attack evaluation and vulnerability-aware defenses.