44 citations · 142 across the 8 of their papers we have counts for
8 papers
Quantitative Propagation of Chaos for SGD in Wide Neural Networks
Valentin De Bortoli, Alain Durmus, Xavier Fontaine +1
In this paper, we investigate the limiting behavior of a continuous-time counterpart of the Stochastic Gradient Descent (SGD) algorithm applied to two-layer overparameterized neura…
Generalized Sliced Distances for Probability Distributions
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli +1
Probability metrics have become an indispensable part of modern statistics and machine learning, and they play a quintessential role in various applications, including statistical…
On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks
Umut Şimşekli, Mert Gürbüzbalaban, Thanh Huy Nguyen +2
The gradient noise (GN) in the stochastic gradient descent (SGD) algorithm is often considered to be Gaussian in the large data regime by assuming that the \emph{classical} central…
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya Urns
Ali Taylan Cemgil, Mehmet Burak Kurutmaz, Sinan Yildirim +2
We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of…
Speech enhancement with variational autoencoders and alpha-stable distributions
Simon Leglaive, Umut Simsekli, Antoine Liutkus +2
This paper focuses on single-channel semi-supervised speech enhancement. We learn a speaker-independent deep generative speech model using the framework of variational autoencoders…
Generalized Sliced Wasserstein Distances
Soheil Kolouri, Kimia Nadjahi, Umut Simsekli +2
The Wasserstein distance and its variations, e.g., the sliced-Wasserstein (SW) distance, have recently drawn attention from the machine learning community. The SW distance, specifi…