141 citations · 285 across the 14 of their papers we have counts for
10 papers · 1 filter
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…
Approximation Algorithms for Cascading Prediction Models
Matthew Streeter
We present an approximation algorithm that takes a pool of pre-trained models as input and produces from it a cascaded model with similar accuracy but lower average-case cost. Appl…
Online Submodular Maximization under a Matroid Constraint with Application to Learning Assignments
Daniel Golovin, Andreas Krause, Matthew Streeter
Which ads should we display in sponsored search in order to maximize our revenue? How should we dynamically rank information sources to maximize the value of the ranking? These app…