12 citations · 17 across the 13 of their papers we have counts for
14 papers
A constructive solution to Talagrand's Gaussian convexification problem
Othmane Mazhar
Talagrand asked for a construction of a large convex subset of a bounded Minkowski sum of a large Gaussian set. We first prove a stronger nonsymmetric existential statement: if $A\…
Direct Estimation of Schrödinger Bridge Time-Series Drifts: Finite-Sample, Asymptotic, and Adaptive Guarantees
Othmane Mazhar, Huyên Pham
We study nonparametric estimation of Schrödinger bridge (SB) drifts from i.i.d.\ data observed on a single time interval. Starting from the conditional-ratio form of the Schrödinge…
SBBTS: A Unified Schrödinger-Bass Framework for Synthetic Financial Time Series
Alexandre Alouadi, Grégoire Loeper, Célian Marsala +2
We study the problem of generating synthetic time series that reproduce both marginal distributions and temporal dynamics, a central challenge in financial machine learning. Existi…
Bridging Schrödinger and Bass: A Semimartingale Optimal Transport Problem with Diffusion Control
Pierre Henry-Labordere, Grégoire Loeper, Othmane Mazhar +2
We study a semimartingale optimal transport problem interpolating between the Schrödinger bridge and the stretched Brownian motion associated with the Bass solution of the Skorokho…
A PDE Derivation of the Schrödinger--Bass Bridge
Alexandre Alouadi, Pierre Henry-Labordère, Grégoire Loeper +3
This short paper announces the main results of \cite{SBB2026}, where the Schrödinger--Bass Bridge (SBB) problem is introduced and studied in full generality. Here we provide a dire…
LightSBB-M: Bridging Schrödinger and Bass for Generative Diffusion Modeling
Alexandre Alouadi, Pierre Henry-Labordère, Grégoire Loeper +3
The Schrodinger Bridge and Bass (SBB) formulation, which jointly controls drift and volatility, is an established extension of the classical Schrodinger Bridge (SB). Building on th…
Monte Carlo-Type Neural Operator for Differential Equations
Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar +1
The Monte Carlo-type Neural Operator (MCNO) introduces a framework for learning solution operators of one-dimensional partial differential equations (PDEs) by directly learning the…
Learning Solution Operators for Partial Differential Equations via Monte Carlo-Type Approximation
Salah Eddine Choutri, Prajwal Chauhan, Othmane Mazhar +1
The Monte Carlo-type Neural Operator (MCNO) introduces a lightweight architecture for learning solution operators for parametric PDEs by directly approximating the kernel integral…
Regret Analysis of Learning-Based Linear Quadratic Gaussian Control with Additive Exploration
Archith Athrey, Othmane Mazhar, Meichen Guo +2
In this paper, we analyze the regret incurred by a computationally efficient exploration strategy, known as naive exploration, for controlling unknown partially observable systems…
Finite-sample analysis of identification of switched linear systems with arbitrary or restricted switching
Shengling Shi, Othmane Mazhar, Bart De Schutter
For the identification of switched systems with a measured switching signal, this work aims to analyze the effect of switching strategies on the estimation error. The data for iden…
Efficient learning of hidden state LTI state space models of unknown order
Boualem Djehiche, Othmane Mazhar
The aim of this paper is to address two related estimation problems arising in the setup of hidden state linear time invariant (LTI) state space systems when the dimension of the h…
Non asymptotic estimation lower bounds for LTI state space models with Cramér-Rao and van Trees
Boualem Djehiche, Othmane Mazhar
We study the estimation problem for linear time-invariant (LTI) state-space models with Gaussian excitation of an unknown covariance. We provide non asymptotic lower bounds for the…
Bayesian Model Selection for Change Point Detection and Clustering
Othmane Mazhar, Cristian R. Rojas, Carlo Fischione +1
We address the new problem of estimating a piece-wise constant signal with the purpose of detecting its change points and the levels of clusters. Our approach is to model it as a n…
Finite impulse response models: A non-asymptotic analysis of the least squares estimator
Boualem Djehiche, Othmane Mazhar, Cristian R. Rojas
We consider a finite impulse response system with centered independent sub-Gaussian design covariates and noise components that are not necessarily identically distributed. We deri…