Bias detection between two histograms can be done by checking a random subset of bins, with a PAC guarantee that depends on the fraction of violating bins.
On the rate of convergence of empirical measure in $\infty-$Wasserstein distance for unbounded density function
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
We consider a sequence of identically independently distributed random samples from an absolutely continuous probability measure in one dimension with unbounded density. We establish a new rate of convergence of the $\infty-$Wasserstein distance between the empirical measure of the samples and the true distribution, which extends the previous convergence result by Trilllos and Slep\v{c}ev to the case that the true distribution has an unbounded density.
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 1years
2025 1verdicts
REJECT 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Sample Complexity of Bias Detection with Subsampled Point-to-Subspace Distances
Bias detection between two histograms can be done by checking a random subset of bins, with a PAC guarantee that depends on the fraction of violating bins.