6 citations · 23 across the 10 of their papers we have counts for
10 papers
Conditional simulation via entropic optimal transport: Toward non-parametric estimation of conditional Brenier maps
Ricardo Baptista, Aram-Alexandre Pooladian, Michael Brennan +2
Conditional simulation is a fundamental task in statistical modeling: Generate samples from the conditionals given finitely many data points from a joint distribution. One promisin…
Plug-in estimation of Schrödinger bridges
Aram-Alexandre Pooladian, Jonathan Niles-Weed
We propose a procedure for estimating the Schrödinger bridge between two probability distributions. Unlike existing approaches, our method does not require iteratively simulating f…
Progressive Entropic Optimal Transport Solvers
Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein +3
Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets. In this context, given two large point clouds…
Tight stability bounds for entropic Brenier maps
Vincent Divol, Jonathan Niles-Weed, Aram-Alexandre Pooladian
Entropic Brenier maps are regularized analogues of Brenier maps (optimal transport maps) which converge to Brenier maps as the regularization parameter shrinks. In this work, we pr…
Learning Elastic Costs to Shape Monge Displacements
Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin +3
Given a source and a target probability measure supported on , the Monge problem asks to find the most efficient way to map one distribution to the other. This effici…
Minimax estimation of discontinuous optimal transport maps: The semi-discrete case
Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed
We consider the problem of estimating the optimal transport map between two probability distributions, and in , on the basis of i.i.d. samples. All existing st…
Optimal transport map estimation in general function spaces
Vincent Divol, Jonathan Niles-Weed, Aram-Alexandre Pooladian
We study the problem of estimating a function given independent samples from a distribution and from the pushforward distribution . This setting is motivated by…
Debiaser Beware: Pitfalls of Centering Regularized Transport Maps
Aram-Alexandre Pooladian, Marco Cuturi, Jonathan Niles-Weed
Estimating optimal transport (OT) maps (a.k.a. Monge maps) between two measures and is a problem fraught with computational and statistical challenges. A promising approach…
Entropic estimation of optimal transport maps
Aram-Alexandre Pooladian, Jonathan Niles-Weed
We develop a computationally tractable method for estimating the optimal map between two distributions over with rigorous finite-sample guarantees. Leveraging an ent…
Supervised Quantile Normalization for Low-rank Matrix Approximation
Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed +1
Low rank matrix factorization is a fundamental building block in machine learning, used for instance to summarize gene expression profile data or word-document counts. To be robust…