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Found 5 papers, 1 papers with code
Trade-off Between Dependence and Complexity for Nonparametric Learning -- an Empirical Process Approach
Our main result shows that a non-trivial trade-off between the complexity of the underlying function class and the dependence among the observations characterizes the learning rate in a large class of nonparametric problems.
Wasserstein Mirror Gradient Flow as the limit of the Sinkhorn Algorithm
This limit, which we call the Sinkhorn flow, is an example of a Wasserstein mirror gradient flow, a concept we introduce here inspired by the well-known Euclidean mirror gradient flows.
Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections
We illustrate the usefulness of this stability estimate by first providing rates of convergence for the natural discrete-discrete and semi-discrete estimators of optimal transport maps.
Detecting Structured Signals in Ising Models
In this paper, we study the effect of dependence on detecting a class of signals in Ising models, where the signals are present in a structured way.
Probability Statistics Theory Statistics Theory 62G10, 62G20, 62C20
Two-component Mixture Model in the Presence of Covariates
We propose a tuning parameter-free nonparametric maximum likelihood approach, implementable via the EM algorithm, to estimate the unknown parameters.
Methodology
