Dario Shariatian

Dario Shariatian

PhD Student, working on generative models

Publications

You can also find my articles on my Google Scholar profile.

Preprints

Denoising Levy Probabilistic Models (DLPM)

Published in arxiv, 2024

This paper introduces a novel framework to use heavy-tailed noise in the denoising diffusion paradigm, which constitutes a generalization of the original DDPM method. Using heavy-tailed noise is shown to bring benefits in various contexts: heavy-tailed data distributions, better robustness to class imbalance, and smaller computational time.

Shariatian, D., Simsekli, U., & Durmus, A.O. (2024). Denoising Lévy Probabilistic Models. ArXiv, abs/2407.18609.
See paper | See slides | GitHub Repository

Conference Papers

Piecewise deterministic generative models

Published in Neurips, 2024

We introduce a novel class of generative models based on piecewise deterministic Markov processes (PDMPs), which combine deterministic motion with random jumps. Like diffusions, PDMPs can be reversed in time. We derive explicit expressions for jump rates and kernels in the time-reversed processes and propose efficient training methods and approximate simulation techniques. Additionally, we provide bounds on the total variation distance between the data and model distributions, supported by promising numerical simulations.

Bertazzi, A., Shariatian, D., Durmus, A.O., Simsekli, U., & Moulines, É. (2024). Piecewise deterministic generative models. Neurips24
See paper | GitHub Repository