4 papers
Do We Need All the Synthetic Data? Targeted Image Augmentation via Diffusion Models
Dang Nguyen, Jiping Li, Jinghao Zheng +1
Synthetically augmenting training datasets with diffusion models has become an effective strategy for improving the generalization of image classifiers. However, existing approache…
Data Distribution as a Lever for Guiding Optimizers Toward Superior Generalization in LLMs
Tushaar Gangavarapu, Jiping Li, Christopher Vattheuer +2
Can modifying the training data distribution guide optimizers toward solutions with improved generalization when training large language models (LLMs)? In this work, we theoretical…
Optimal Watermark Generation under Type I and Type II Errors
Hengzhi He, Shirong Xu, Alexander Nemecek +3
Watermarking has recently emerged as a crucial tool for protecting the intellectual property of generative models and for distinguishing AI-generated content from human-generated d…
Representations Shape Weak-to-Strong Generalization: Theoretical Insights and Empirical Predictions
Yihao Xue, Jiping Li, Baharan Mirzasoleiman
Weak-to-Strong Generalization (W2SG), where a weak model supervises a stronger one, serves as an important analogy for understanding how humans might guide superhuman intelligence…