publications

2026

1. SPIGM@ICML

   Latent-Augmented Discrete Diffusion Models (LADD)

   Dario Shariatian, Alain Durmus, Umut Simsekli, and 1 more author

   In ICML 2026 Workshop on Structured Probabilistic Inference & Generative Modeling, May 2026

   Abs arXiv Bib Code

   We introduce a learnable auxiliary latent channel for discrete diffusion, enabling cross-token modeling and improving few-step generation.

   @inproceedings{shariatian2026ladd,
     title = {Latent-Augmented Discrete Diffusion Models (LADD)},
     author = {Shariatian, Dario and Durmus, Alain and Simsekli, Umut and Peluchetti, Stefano},
     booktitle = {ICML 2026 Workshop on Structured Probabilistic Inference {\&} Generative Modeling},
     year = {2026},
     month = may,
   }

2. arXiv

   Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation

   Samson Gourevitch, Yazid Janati, Dario Shariatian, and 4 more authors

   arXiv preprint, May 2026

   Abs arXiv Bib Code

   We revisit uniform diffusion models, identify a leave-one-out denoising target, derive conversions between parameterizations, and introduce an absorbing-state reformulation with improved sampling behavior.

   @article{gourevitch2026uniform,
     title = {Uniform Diffusion Models Revisited: Leave-One-Out Denoiser and Absorbing State Reformulation},
     author = {Gourevitch, Samson and Janati, Yazid and Shariatian, Dario and Simsekli, Umut and Moulines, {\'E}ric and Xing, Eric P. and Durmus, Alain},
     journal = {arXiv preprint},
     year = {2026},
     month = may,
   }

2025

1. NeurIPS

   Algorithm and Data Dependent Generalization Bounds for Score-Based Generative Models

   Dario Shariatian, Benjamin Dupuis, Maxime Haddouche, and 2 more authors

   In Advances in Neural Information Processing Systems, Sep 2025

   Abs arXiv Bib Code

   We establish generalization bounds for diffusion models that account jointly for optimization dynamics and data distribution, together with supporting empirical results.

   @inproceedings{shariatian2025generalization,
     title = {Algorithm and Data Dependent Generalization Bounds for Score-Based Generative Models},
     author = {Shariatian, Dario and Dupuis, Benjamin and Haddouche, Maxime and Durmus, Alain and Simsekli, Umut},
     booktitle = {Advances in Neural Information Processing Systems},
     year = {2025},
     month = sep,
   }

2. ICML

   Bit-Level Discrete Diffusion with Markov Probabilistic Models: An Improved Framework with Sharp Convergence Bounds under Minimal Assumptions

   Dario Shariatian, Le-Tuyet-Nhi Pham, Antonio Ocello, and 2 more authors

   In International Conference on Machine Learning, Feb 2025

   Abs arXiv Bib Code

   We develop a discrete diffusion method for bit data that outperforms MD4 and Discrete Flow Matching on binarized MNIST while using about 2.5x fewer network evaluations, with supporting theoretical analysis.

   @inproceedings{shariatian2025dmpm,
     title = {Bit-Level Discrete Diffusion with Markov Probabilistic Models: An Improved Framework with Sharp Convergence Bounds under Minimal Assumptions},
     author = {Shariatian, Dario and Pham, Le-Tuyet-Nhi and Ocello, Antonio and Conforti, Giovanni and Durmus, Alain},
     booktitle = {International Conference on Machine Learning},
     year = {2025},
     month = feb,
   }

3. ICLR

   Heavy-Tailed Diffusion with Denoising Levy Probabilistic Models

   Dario Shariatian, Umut Simsekli, and Alain Durmus

   In International Conference on Learning Representations, Jan 2025

   Abs arXiv Bib Code Slides

   We develop a diffusion-model framework for better modeling heavy-tailed and imbalanced data.

   @inproceedings{shariatian2025dlpm,
     title = {Heavy-Tailed Diffusion with Denoising Levy Probabilistic Models},
     author = {Shariatian, Dario and Simsekli, Umut and Durmus, Alain},
     booktitle = {International Conference on Learning Representations},
     year = {2025},
     month = jan,
   }

2024

1. NeurIPS

   Piecewise Deterministic Generative Models

   Andrea Bertazzi, Dario Shariatian, Umut Simsekli, and 2 more authors

   In Advances in Neural Information Processing Systems, Jul 2024

   Abs arXiv Bib Code

   We introduce a class of generative models based on piecewise-deterministic Markov processes, combining deterministic motion with random jumps, and report promising empirical results.

   @inproceedings{bertazzi2024pdmp,
     title = {Piecewise Deterministic Generative Models},
     author = {Bertazzi, Andrea and Shariatian, Dario and Simsekli, Umut and Moulines, {\'E}ric and Durmus, Alain},
     booktitle = {Advances in Neural Information Processing Systems},
     year = {2024},
     month = jul,
   }