Search Results for author: Veeranjaneyulu Sadhanala

Found 8 papers, 0 papers with code

Multivariate Trend Filtering for Lattice Data

no code implementations29 Dec 2021 Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Addison J. Hu, Ryan J. Tibshirani

We study a multivariate version of trend filtering, called Kronecker trend filtering or KTF, for the case in which the design points form a lattice in $d$ dimensions.

A Higher-Order Kolmogorov-Smirnov Test

no code implementations24 Mar 2019 Veeranjaneyulu Sadhanala, Yu-Xiang Wang, Aaditya Ramdas, Ryan J. Tibshirani

We present an extension of the Kolmogorov-Smirnov (KS) two-sample test, which can be more sensitive to differences in the tails.

Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods

no code implementations NeurIPS 2017 Veeranjaneyulu Sadhanala, Yu-Xiang Wang, James L. Sharpnack, Ryan J. Tibshirani

To move past this, we define two new higher-order TV classes, based on two ways of compiling the discrete derivatives of a parameter across the nodes.

Additive Models with Trend Filtering

no code implementations16 Feb 2017 Veeranjaneyulu Sadhanala, Ryan J. Tibshirani

We study additive models built with trend filtering, i. e., additive models whose components are each regularized by the (discrete) total variation of their $k$th (discrete) derivative, for a chosen integer $k \geq 0$.

Additive models

Parallel and Distributed Block-Coordinate Frank-Wolfe Algorithms

no code implementations22 Sep 2014 Yu-Xiang Wang, Veeranjaneyulu Sadhanala, Wei Dai, Willie Neiswanger, Suvrit Sra, Eric P. Xing

We develop parallel and distributed Frank-Wolfe algorithms; the former on shared memory machines with mini-batching, and the latter in a delayed update framework.

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