1 citations · 2 across the 7 of their papers we have counts for
5 papers · 1 filter
Latent Guided Sampling for Combinatorial Optimization
Sobihan Surendran, Adeline Fermanian, Sylvain Le Corff
Combinatorial Optimization problems are widespread in domains such as logistics, manufacturing, and drug discovery, yet their NP-hard nature makes them computationally challenging.…
Multivariate Online Linear Regression for Hierarchical Forecasting
Massil Hihat, Guillaume Garrigos, Adeline Fermanian +1
In this paper, we consider a deterministic online linear regression model where we allow the responses to be multivariate. To address this problem, we introduce MultiVAW, a method…
Non-asymptotic Analysis of Biased Adaptive Stochastic Approximation
Sobihan Surendran, Antoine Godichon-Baggioni, Adeline Fermanian +1
Stochastic Gradient Descent (SGD) with adaptive steps is widely used to train deep neural networks and generative models. Most theoretical results assume that it is possible to obt…
Dynamical Survival Analysis with Controlled Latent States
Linus Bleistein, Van-Tuan Nguyen, Adeline Fermanian +1
We consider the task of learning individual-specific intensities of counting processes from a set of static variables and irregularly sampled time series. We introduce a novel mode…
Learning the Dynamics of Sparsely Observed Interacting Systems
Linus Bleistein, Adeline Fermanian, Anne-Sophie Jannot +1
We address the problem of learning the dynamics of an unknown non-parametric system linking a target and a feature time series. The feature time series is measured on a sparse and…