8 papers
Does Normalization Choice Matter for Causal Large Time-Series Models?
Samy-Melwan Vilhes, Gilles Gasso, Mokhtar Z Alaya
Large models for time-series forecasting have been emerged as a promising paradigm for training models on heterogeneous collections of signals. These models typically rely on causa…
A Unified Kantorovich Duality for Multimarginal Optimal Transport
Yehya Cheryala, Mokhtar Z. Alaya, Salim Bouzebda
Multimarginal optimal transport (MOT) has gained increasing attention in recent years, notably due to its relevance in machine learning and statistics, where one seeks to jointly c…
Bounds in Wasserstein Distance for Locally Stationary Functional Time Series
Jan Nino G. Tinio, Mokhtar Z. Alaya, Salim Bouzebda
Functional time series (FTS) extend traditional methodologies to accommodate data observed as functions/curves. A significant challenge in FTS consists of accurately capturing the…
Sparsified-Learning for High-Dimensional Heavy-Tailed Locally Stationary Time Series, Concentration and Oracle Inequalities
Yingjie Wang, Mokhtar Z. Alaya, Salim Bouzebda +1
Sparse learning is ubiquitous in many machine learning tasks. It aims to regularize the goodness-of-fit objective by adding a penalty term to encode structural constraints on the m…
Bounds in Wasserstein Distance for Locally Stationary Processes
Jan Nino G. Tinio, Mokhtar Z. Alaya, Salim Bouzebda
Locally stationary (LSPs) constitute an essential modeling paradigm for capturing the nuanced dynamics inherent in time series data whose statistical characteristics, including mea…
Gaussian-Smoothed Sliced Probability Divergences
Mokhtar Z. Alaya, Alain Rakotomamonjy, Maxime Berar +1
Gaussian smoothed sliced Wasserstein distance has been recently introduced for comparing probability distributions, while preserving privacy on the data. It has been shown that it…