Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers

26 May 2016

Papers citing "Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers"

12 / 12 papers shown

Title
Dynamic angular synchronization under smoothness constraints Ernesto Araya Mihai Cucuringu Hemant Tyagi 45 0 0 06 Jun 2024
Total Variation Graph Neural Networks Jonas Hansen F. Bianchi 33 12 0 11 Nov 2022
Exponential Family Trend Filtering on Lattices Veeranjaneyulu Sadhanala R. Bassett James Sharpnack D. McDonald 35 3 0 19 Sep 2022
Rates of Convergence for Regression with the Graph Poly-Laplacian Nicolas García Trillos Ryan W. Murray Matthew Thorpe 32 4 0 06 Sep 2022
Spatially Adaptive Online Prediction of Piecewise Regular Functions S. Chatterjee Subhajit Goswami OffRL 26 1 0 30 Mar 2022
Multivariate Trend Filtering for Lattice Data Veeranjaneyulu Sadhanala Yu-Xiang Wang Addison J. Hu R. Tibshirani 21 8 0 29 Dec 2021
An Optimal Reduction of TV-Denoising to Adaptive Online Learning Dheeraj Baby Xuandong Zhao Yu-Xiang Wang 19 11 0 23 Jan 2021
Adaptive Online Estimation of Piecewise Polynomial Trends Dheeraj Baby Yu-Xiang Wang 28 12 0 30 Sep 2020
Total variation multiscale estimators for linear inverse problems Miguel del Alamo Axel Munk 32 6 0 21 May 2019
Graph-based regularization for regression problems with alignment and highly-correlated designs Yuan Li Benjamin Mark Garvesh Raskutti Rebecca Willett Hyebin Song David Neiman 6 23 0 20 Mar 2018
Approximate $l_0$ -penalized estimation of piecewise-constant signals on graphs Z. Fan Leying Guan 31 21 0 04 Mar 2017
The DFS Fused Lasso: Linear-Time Denoising over General Graphs Oscar Hernan Madrid Padilla James G. Scott James Sharpnack R. Tibshirani FedML 36 67 0 11 Aug 2016