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Found 4 papers, 1 papers with code
Differentially Private Representation Learning via Image Captioning
In this work, we show that effective DP representation learning can be done via image captioning and scaling up to internet-scale multimodal datasets.
Watermarking Makes Language Models Radioactive
This paper investigates the radioactivity of LLM-generated texts, i. e. whether it is possible to detect that such input was used as training data.
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training
We first show that the phenomenon extends to Noisy-SGD (DP-SGD without clipping), suggesting that the stochasticity (and not the clipping) is the cause of this implicit bias, even with additional isotropic Gaussian noise.
TAN Without a Burn: Scaling Laws of DP-SGD
Differentially Private methods for training Deep Neural Networks (DNNs) have progressed recently, in particular with the use of massive batches and aggregated data augmentations for a large number of training steps.
