no code implementations • ICML 2020 • Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert

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.

no code implementations • 7 Jun 2024 • Parnian Kassraie, Aram-Alexandre Pooladian, Michal Klein, James Thornton, Jonathan Niles-Weed, Marco Cuturi

Optimal transport (OT) has profoundly impacted machine learning by providing theoretical and computational tools to realign datasets.

no code implementations • 20 Jun 2023 • Michal Klein, Aram-Alexandre Pooladian, Pierre Ablin, Eugène Ndiaye, Jonathan Niles-Weed, Marco Cuturi

Given a source and a target probability measure supported on $\mathbb{R}^d$, the Monge problem asks to find the most efficient way to map one distribution to the other.

no code implementations • NeurIPS 2023 • Natalie Frank, Jonathan Niles-Weed

We study the consistency of surrogate risks for robust binary classification.

no code implementations • 26 Jan 2023 • Aram-Alexandre Pooladian, Vincent Divol, Jonathan Niles-Weed

We consider the problem of estimating the optimal transport map between two probability distributions, $P$ and $Q$ in $\mathbb R^d$, on the basis of i. i. d.

no code implementations • 7 Dec 2022 • Vincent Divol, Jonathan Niles-Weed, Aram-Alexandre Pooladian

To ensure identifiability, we assume that $T = \nabla \varphi_0$ is the gradient of a convex function, in which case $T$ is known as an \emph{optimal transport map}.

no code implementations • 29 Oct 2022 • Tom Tirer, Haoxiang Huang, Jonathan Niles-Weed

In this paper, we propose a richer model that can capture this phenomenon by forcing the features to stay in the vicinity of a predefined features matrix (e. g., intermediate features).

no code implementations • 26 Jun 2022 • Xin Bing, Florentina Bunea, Jonathan Niles-Weed

Our results establish this metric to be a canonical choice.

no code implementations • 18 Jun 2022 • Natalie S. Frank, Jonathan Niles-Weed

Adversarial training is one of the most popular methods for training methods robust to adversarial attacks, however, it is not well-understood from a theoretical perspective.

no code implementations • 18 Jun 2022 • Natalie S. Frank, Jonathan Niles-Weed

Robustness to adversarial perturbations is of paramount concern in modern machine learning.

no code implementations • 19 Apr 2022 • Eustasio del Barrio, Alberto Gonzalez-Sanz, Jean-Michel Loubes, Jonathan Niles-Weed

We prove a central limit theorem for the entropic transportation cost between subgaussian probability measures, centered at the population cost.

no code implementations • 21 Nov 2021 • Sheng Liu, Aakash Kaku, Weicheng Zhu, Matan Leibovich, Sreyas Mohan, Boyang Yu, Haoxiang Huang, Laure Zanna, Narges Razavian, Jonathan Niles-Weed, Carlos Fernandez-Granda

Reliable probability estimation is of crucial importance in many real-world applications where there is inherent (aleatoric) uncertainty.

no code implementations • 24 Sep 2021 • Aram-Alexandre Pooladian, Jonathan Niles-Weed

We develop a computationally tractable method for estimating the optimal map between two distributions over $\mathbb{R}^d$ with rigorous finite-sample guarantees.

1 code implementation • 26 Jul 2021 • Tudor Manole, Sivaraman Balakrishnan, Jonathan Niles-Weed, Larry Wasserman

Our work also provides new bounds on the risk of corresponding plugin estimators for the quadratic Wasserstein distance, and we show how this problem relates to that of estimating optimal transport maps using stability arguments for smooth and strongly convex Brenier potentials.

no code implementations • 24 Feb 2021 • Jonathan Niles-Weed, Ilias Zadik

We establish a phase transition known as the "all-or-nothing" phenomenon for noiseless discrete channels.

Statistics Theory Information Theory Information Theory Probability Statistics Theory

no code implementations • 6 Feb 2021 • De Huang, Jonathan Niles-Weed, Rachel Ward

We analyze Oja's algorithm for streaming $k$-PCA and prove that it achieves performance nearly matching that of an optimal offline algorithm.

no code implementations • 11 Jan 2021 • Dylan J. Altschuler, Jonathan Niles-Weed

A recent approach to the Beck-Fiala conjecture, a fundamental problem in combinatorics, has been to understand when random integer matrices have constant discrepancy.

Probability Discrete Mathematics Combinatorics

no code implementations • 30 Jun 2020 • Gonzalo Mena, Amin Nejatbakhsh, Erdem Varol, Jonathan Niles-Weed

We study Sinkhorn EM (sEM), a variant of the expectation maximization (EM) algorithm for mixtures based on entropic optimal transport.

2 code implementations • NeurIPS 2020 • Sheng Liu, Jonathan Niles-Weed, Narges Razavian, Carlos Fernandez-Granda

In contrast with existing approaches, which use the model output during early learning to detect the examples with clean labels, and either ignore or attempt to correct the false labels, we take a different route and instead capitalize on early learning via regularization.

Ranked #4 on Learning with noisy labels on CIFAR-10N-Random2

no code implementations • 8 Feb 2020 • Marco Cuturi, Olivier Teboul, Jonathan Niles-Weed, Jean-Philippe Vert

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.

no code implementations • NeurIPS 2019 • Jason Altschuler, Francis Bach, Alessandro Rudi, Jonathan Niles-Weed

The Sinkhorn "distance", a variant of the Wasserstein distance with entropic regularization, is an increasingly popular tool in machine learning and statistical inference.

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