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Found 7 papers, 1 papers with code
Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem
We prove several fundamental statistical bounds for entropic OT with the squared Euclidean cost between subgaussian probability measures in arbitrary dimension.
Uncoupled isotonic regression via minimum Wasserstein deconvolution
Isotonic regression is a standard problem in shape-constrained estimation where the goal is to estimate an unknown nondecreasing regression function $f$ from independent pairs $(x_i, y_i)$ where $\mathbb{E}[y_i]=f(x_i), i=1, \ldots n$.
Statistical Optimal Transport via Factored Couplings
We propose a new method to estimate Wasserstein distances and optimal transport plans between two probability distributions from samples in high dimension.
An explicit analysis of the entropic penalty in linear programming
We close this long-standing gap in the literature regarding entropic penalization by giving a new proof of the exponential convergence, valid for any linear program.
Minimax Rates and Efficient Algorithms for Noisy Sorting
There has been a recent surge of interest in studying permutation-based models for ranking from pairwise comparison data.
Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration
Computing optimal transport distances such as the earth mover's distance is a fundamental problem in machine learning, statistics, and computer vision.
Online learning in repeated auctions
To our knowledge, this is the first complete set of strategies for bidders participating in auctions of this type.
