| Selected Publications Local-Global MCMC kernels: the best of both worlds [pdf] Samsonov S., Lagutin E., Gabrié M., Durmus A., Naumov A., Moulines E. (NeurIPS 2022) Adaptive Monte Carlo augmented with normalizing flows [pdf] Gabrié M., Rotskoff G. M., Vanden-Eijnden E. (PNAS 2022) Adaptation of the Independent Metropolis-Hastings Sampler with Normalizing Flow Proposals. [pdf] Brofos J. A., Gabrié M., Brubaker M. A., & Lederman R. R. (AISTAT 2022) Efficient Bayesian Sampling Using Normalizing Flows to Assist Markov Chain Monte Carlo Methods. [pdf] Gabrié M., Rotskoff G. M., & Vanden-Eijnden E. Invertible Neural Networks, NormalizingFlows, and Explicit Likelihood Models (ICML Workshop) (2021) - Accepted for contributed talk. On the interplay between data structure and loss function in classification problems [pdf] d'Ascoli S., Gabrié M., Sagun L., Biroli G. (NeurIPS 2021) Mean-field inference methods for neural networks, [pdf] Gabrié, M. Journal of Physics A: Mathematical and Theoretical, 53(22), 1–58. (2020) Entropy and mutual information in models of deep neural networks, [pdf] [spotlight short video] Gabrié, M., Manoel, A., Luneau, C., Barbier, J., Macris, N., Krzakala, F., & Zdeborová, L. Advances in Neural Information Processing Systems 31, 1826--1836 (2018) |