1 citations · 1 across the 7 of their papers we have counts for
7 papers
Sliding-Window Signatures as Features for Time Series Forecasting
Nina Drobac, Margaux Brégère, Margaux Br{é}g{è}re +2
The evolution of a time series is often influenced not only by its past values but also by external covariates, giving rise to forecasting with exogenous variables. Nonlinear and d…
Online Episodic Convex Reinforcement Learning
Bianca Marin Moreno, Khaled Eldowa, Pierre Gaillard +2
We study online learning in episodic finite-horizon Markov decision processes (MDPs) with convex objective functions, known as the concave utility reinforcement learning (CURL) pro…
AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting
Keshav Das, Julie Keisler, Margaux Brégère +1
Electricity demand forecasting is key to ensuring that supply meets demand lest the grid would blackout. Reliable short-term forecasts may be obtained by combining a Generalized Ad…
MetaCURL: Non-stationary Concave Utility Reinforcement Learning
Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard +1
We explore online learning in episodic loop-free Markov decision processes on non-stationary environments (changing losses and probability transitions). Our focus is on the Concave…
Automated Deep Learning for Load Forecasting
Julie Keisler, Sandra Claudel, Gilles Cabriel +1
Accurate forecasting of electricity consumption is essential to ensure the performance and stability of the grid, especially as the use of renewable energy increases. Forecasting e…
Efficient Model-Based Concave Utility Reinforcement Learning through Greedy Mirror Descent
Bianca Marin Moreno, Margaux Brégère, Pierre Gaillard +1
Many machine learning tasks can be solved by minimizing a convex function of an occupancy measure over the policies that generate them. These include reinforcement learning, imitat…
A multivariate variable selection approach for analyzing LC-MS metabolomics data
M. Perrot-Dockès, C. Lévy-Leduc, J. Chiquet +5
Omic data are characterized by the presence of strong dependence structures that result either from data acquisition or from some underlying biological processes. In metabolomics,…