3 citations · 7 across the 11 of their papers we have counts for
6 papers · 1 filter
Watermark Anything with Localized Messages
Tom Sander, Pierre Fernandez, Alain Durmus +2
Image watermarking methods are not tailored to handle small watermarked areas. This restricts applications in real-world scenarios where parts of the image may come from different…
Piecewise deterministic generative models
Andrea Bertazzi, Dario Shariatian, Umut Simsekli +2
We introduce a novel class of generative models based on piecewise deterministic Markov processes (PDMPs), a family of non-diffusive stochastic processes consisting of deterministi…
Heavy-Tailed Diffusion with Denoising Lévy Probabilistic Models
Dario Shariatian, Umut Simsekli, Alain Durmus
Exploring noise distributions beyond Gaussian in diffusion models remains an open challenge. While Gaussian-based models succeed within a unified SDE framework, recent studies sugg…
Differentially Private Representation Learning via Image Captioning
Tom Sander, Yaodong Yu, Maziar Sanjabi +4
Differentially private (DP) machine learning is considered the gold-standard solution for training a model from sensitive data while still preserving privacy. However, a major barr…
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training
Tom Sander, Maxime Sylvestre, Alain Durmus
Training Deep Neural Networks (DNNs) with small batches using Stochastic Gradient Descent (SGD) yields superior test performance compared to larger batches. The specific noise stru…
Watermarking Makes Language Models Radioactive
Tom Sander, Pierre Fernandez, Alain Durmus +2
We investigate the radioactivity of text generated by large language models (LLM), i.e. whether it is possible to detect that such synthetic input was used to train a subsequent LL…