42 citations · 319 across the 36 of their papers we have counts for
5 papers · 2 filters
Correcting the bias in least squares regression with volume-rescaled sampling
Michał Dereziński, Manfred K. Warmuth, Daniel Hsu
Consider linear regression where the examples are generated by an unknown distribution on . Without any assumptions on the noise, the linear least squares solution for…
Reverse iterative volume sampling for linear regression
Michał Dereziński, Manfred K. Warmuth
We study the following basic machine learning task: Given a fixed set of -dimensional input points for a linear regression problem, we wish to predict a hidden response value fo…
Online Non-Additive Path Learning under Full and Partial Information
Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri +2
We study the problem of online path learning with non-additive gains, which is a central problem appearing in several applications, including ensemble structured prediction. We pre…
A more globally accurate dimensionality reduction method using triplets
Ehsan Amid, Manfred K. Warmuth
We first show that the commonly used dimensionality reduction (DR) methods such as t-SNE and LargeVis poorly capture the global structure of the data in the low dimensional embeddi…
Leveraged volume sampling for linear regression
Michał Dereziński, Manfred K. Warmuth, Daniel Hsu
Suppose an design matrix in a linear regression problem is given, but the response for each point is hidden unless explicitly requested. The goal is to sample only a s…