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
We study Kantorovich duality for multimarginal optimal transport (MOT) with bounded continuous cost functions. The main focus is not only the equality between the primal and dual v…
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…
Heterogeneous Wasserstein Discrepancy for Incomparable Distributions
Mokhtar Z. Alaya, Gilles Gasso, Maxime Berar +1
Optimal Transport (OT) metrics allow for defining discrepancies between two probability measures. Wasserstein distance is for longer the celebrated OT-distance frequently-used in t…
Binacox: automatic cut-point detection in high-dimensional Cox model with applications in genetics
Simon Bussy, Mokhtar Z. Alaya, Anne-Sophie Jannot +1
We introduce the binacox, a prognostic method to deal with the problem of detecting multiple cut-points per features in a multivariate setting where a large number of continuous fe…