8 citations · 22 across the 6 of their papers we have counts for
5 papers · 1 filter
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
Alain Durmus, Eric Moulines, Alexey Naumov +3
This paper provides a non-asymptotic analysis of linear stochastic approximation (LSA) algorithms with fixed stepsize. This family of methods arises in many machine learning tasks…
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Igor Colin, Ludovic Dos Santos, Kevin Scaman
We investigate the theoretical limits of pipeline parallel learning of deep learning architectures, a distributed setup in which the computation is distributed per layer instead of…
Lipschitz regularity of deep neural networks: analysis and efficient estimation
Kevin Scaman, Aladin Virmaux
Deep neural networks are notorious for being sensitive to small well-chosen perturbations, and estimating the regularity of such architectures is of utmost importance for safe and…
A Spectral Method for Activity Shaping in Continuous-Time Information Cascades
Kevin Scaman, Argyris Kalogeratos, Luca Corinzia +1
Information Cascades Model captures dynamical properties of user activity in a social network. In this work, we develop a novel framework for activity shaping under the Continuous-…
Multivariate Hawkes Processes for Large-scale Inference
Rémi Lemonnier, Kevin Scaman, Argyris Kalogeratos
In this paper, we present a framework for fitting multivariate Hawkes processes for large-scale problems both in the number of events in the observed history and the number of…