no code implementations • 5 Sep 2023 • Seunghoon Paik, Michael Celentano, Alden Green, Ryan J. Tibshirani
Maximum mean discrepancy (MMD) refers to a general class of nonparametric two-sample tests that are based on maximizing the mean difference over samples from one distribution $P$ versus another $Q$, over all choices of data transformations $f$ living in some function space $\mathcal{F}$.
no code implementations • 30 Dec 2022 • Addison J. Hu, Alden Green, Ryan J. Tibshirani
We study an estimator that forms the Voronoi diagram of the design points, and then solves an optimization problem that regularizes according to a certain discrete notion of total variation (TV): the sum of weighted absolute differences of parameters $\theta_i,\theta_j$ (which estimate the function values $f_0(x_i), f_0(x_j)$) at all neighboring cells $i, j$ in the Voronoi diagram.
no code implementations • 14 Nov 2021 • Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
We also show that PCR-LE is \emph{manifold adaptive}: that is, we consider the situation where the design is supported on a manifold of small intrinsic dimension $m$, and give upper bounds establishing that PCR-LE achieves the faster minimax estimation ($n^{-2s/(2s + m)}$) and testing ($n^{-4s/(4s + m)}$) rates of convergence.
no code implementations • 3 Jun 2021 • Alden Green, Sivaraman Balakrishnan, Ryan J. Tibshirani
In this paper we study the statistical properties of Laplacian smoothing, a graph-based approach to nonparametric regression.