4 papers
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…
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 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…
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…