44 citations · 206 across the 28 of their papers we have counts for
11 papers · 1 filter
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…
Approximate Bayesian Computation with the Sliced-Wasserstein Distance
Kimia Nadjahi, Valentin De Bortoli, Alain Durmus +2
Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approxima…
Robust Distributed Accelerated Stochastic Gradient Methods for Multi-Agent Networks
Alireza Fallah, Mert Gurbuzbalaban, Asuman Ozdaglar +2
We study distributed stochastic gradient (D-SG) method and its accelerated variant (D-ASG) for solving decentralized strongly convex stochastic optimization problems where the obje…
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Thanh Huy Nguyen, Umut Şimşekli, Mert Gürbüzbalaban +1
Stochastic gradient descent (SGD) has been widely used in machine learning due to its computational efficiency and favorable generalization properties. Recently, it has been empiri…
Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance
Kimia Nadjahi, Alain Durmus, Umut Şimşekli +1
Minimum expected distance estimation (MEDE) algorithms have been widely used for probabilistic models with intractable likelihood functions and they have become increasingly popula…
Probabilistic Permutation Synchronization using the Riemannian Structure of the Birkhoff Polytope
Tolga Birdal, Umut Şimşekli
We present an entirely new geometric and probabilistic approach to synchronization of correspondences across multiple sets of objects or images. In particular, we present two algor…