141 citations · 285 across the 14 of their papers we have counts for
14 papers
Sharp Taylor Polynomial Enclosures in One Dimension
Matthew Streeter, Joshua V. Dillon
It is often useful to have polynomial upper or lower bounds on a one-dimensional function that are valid over a finite interval, called a trust region. A classical way to produce p…
Universal Majorization-Minimization Algorithms
Matthew Streeter
Majorization-minimization (MM) is a family of optimization methods that iteratively reduce a loss by minimizing a locally-tight upper bound, called a majorizer. Traditionally, majo…
Automatically Bounding the Taylor Remainder Series: Tighter Bounds and New Applications
Matthew Streeter, Joshua V. Dillon
We present a new algorithm for automatically bounding the Taylor remainder series. In the special case of a scalar function , our algorithm takes as i…
Learning Effective Loss Functions Efficiently
Matthew Streeter
We consider the problem of learning a loss function which, when minimized over a training dataset, yields a model that approximately minimizes a validation error metric. Though lea…
Bayes Optimal Early Stopping Policies for Black-Box Optimization
Matthew Streeter
We derive an optimal policy for adaptively restarting a randomized algorithm, based on observed features of the run-so-far, so as to minimize the expected time required for the alg…
Learning Optimal Linear Regularizers
Matthew Streeter
We present algorithms for efficiently learning regularizers that improve generalization. Our approach is based on the insight that regularizers can be viewed as upper bounds on the…