REVIEW 1 cited by
Improved rates of convergence for the multivariate Central Limit Theorem in Wasserstein distance
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
abstract
We provide new bounds for the rate of convergence of the multivariate Central Limit Theorem in Wasserstein distances of order $p \geq 2$. In particular, we obtain what we conjecture to be the asymptotically optimal rate whenever the density of the summands admits a non-zero continuous component and has a non-zero third moment.
Forward citations
Cited by 1 Pith paper
-
VAE-DNN: Energy-Efficient Trainable-by-Parts Surrogate Model For Parametric Partial Differential Equations
A trainable-by-parts variational autoencoder with a neural network mapper claims to beat FNO and DeepONet on groundwater flow problems in both accuracy and training efficiency.
Discussion (0). Sign in to comment.