Search Results for author:
Found 7 papers, 0 papers with code
Multivariate Gaussian Approximation for Random Forest via Region-based Stabilization
We derive Gaussian approximation bounds for random forest predictions based on a set of training points given by a Poisson process, under fairly mild regularity assumptions on the data generating process.
Nonsmooth Nonparametric Regression via Fractional Laplacian Eigenmaps
More specifically, our approach is using the fractional Laplacian and is designed to handle the case when the true regression function lies in an $L_2$-fractional Sobolev space with order $s\in (0, 1)$.
Adaptive and non-adaptive minimax rates for weighted Laplacian-eigenmap based nonparametric regression
We show both adaptive and non-adaptive minimax rates of convergence for a family of weighted Laplacian-Eigenmap based nonparametric regression methods, when the true regression function belongs to a Sobolev space and the sampling density is bounded from above and below.
A Flexible Approach for Normal Approximation of Geometric and Topological Statistics
We derive normal approximation results for a class of stabilizing functionals of binomial or Poisson point process, that are not necessarily expressible as sums of certain score functions.
Topologically penalized regression on manifolds
We study a regression problem on a compact manifold M. In order to take advantage of the underlying geometry and topology of the data, the regression task is performed on the basis of the first several eigenfunctions of the Laplace-Beltrami operator of the manifold, that are regularized with topological penalties.
Algorithms for ridge estimation with convergence guarantees
The extraction of filamentary structure from a point cloud is discussed.
Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series
We present an approach to model time series data from resting state fMRI for autism spectrum disorder (ASD) severity classification.
