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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. First, through a new …
cost between subgaussian probability measures in arbitrary dimension. First, through a new …
[PDF][PDF] Statistical optimal transport
Statistical Optimal Transport arxiv:2407.18163v2 [math.ST] 7 Nov 2024 Page 1 Statistical
Optimal Transport Sinho Chewi Yale Jonathan Niles-Weed NYU Philippe Rigollet MIT …
Optimal Transport Sinho Chewi Yale Jonathan Niles-Weed NYU Philippe Rigollet MIT …
On the sample complexity of entropic optimal transport
We study the sample complexity of entropic optimal transport in high dimensions using
computationally efficient plug-in estimators. We significantly advance the state of the art by …
computationally efficient plug-in estimators. We significantly advance the state of the art by …
Estimation of wasserstein distances in the spiked transport model
Estimation of Wasserstein distances in the Spiked Transport Model Page 1 Bernoulli 28(4),
2022, 2663–2688 https://doi.org/10.3150/21-BEJ1433 Estimation of Wasserstein distances …
2022, 2663–2688 https://doi.org/10.3150/21-BEJ1433 Estimation of Wasserstein distances …
An improved central limit theorem and fast convergence rates for entropic transportation costs
We prove a central limit theorem for the entropic transportation cost between subgaussian
probability measures, centered at the population cost. This is the first result which allows for …
probability measures, centered at the population cost. This is the first result which allows for …
Estimating the rate-distortion function by Wasserstein gradient descent
In the theory of lossy compression, the rate-distortion (RD) function $ R (D) $ describes how
much a data source can be compressed (in bit-rate) at any given level of fidelity (distortion) …
much a data source can be compressed (in bit-rate) at any given level of fidelity (distortion) …
Minimax estimation of smooth optimal transport maps
JC Hütter, P Rigollet - 2021 - projecteuclid.org
The supplementary materials contain more background on convex functions, wavelets and
empirical processes, as well as tools to prove lower bounds, alternative assumptions based …
empirical processes, as well as tools to prove lower bounds, alternative assumptions based …
pop-cosmos: A comprehensive picture of the galaxy population from COSMOS data
We present pop-cosmos: a comprehensive model characterizing the galaxy population,
calibrated to 140,938 (r< 25 selected) galaxies from the Cosmic Evolution Survey …
calibrated to 140,938 (r< 25 selected) galaxies from the Cosmic Evolution Survey …
Massively scalable Sinkhorn distances via the Nyström method
The Sinkhorn" distance," a variant of the Wasserstein distance with entropic regularization, is
an increasingly popular tool in machine learning and statistical inference. However, the time …
an increasingly popular tool in machine learning and statistical inference. However, the time …
Statistical analysis of Wasserstein distributionally robust estimators
We consider statistical methods that invoke a min-max distributionally robust formulation to
extract good out-of-sample performance in data-driven optimization and learning problems …
extract good out-of-sample performance in data-driven optimization and learning problems …