21 citations · 33 across the 4 of their papers we have counts for
4 papers
ABCDE: Application-Based Cluster Diff Evals
Stephan van Staden, Alexander Grubb
This paper considers the problem of evaluating clusterings of very large populations of items. Given two clusterings, namely a Baseline clustering and an Experiment clustering, the…
Efficient Feature Group Sequencing for Anytime Linear Prediction
Hanzhang Hu, Alexander Grubb, J. Andrew Bagnell +1
We consider \textit{anytime} linear prediction in the common machine learning setting, where features are in groups that have costs. We achieve anytime (or interruptible) predictio…
SpeedMachines: Anytime Structured Prediction
Alexander Grubb, Daniel Munoz, J. Andrew Bagnell +1
Structured prediction plays a central role in machine learning applications from computational biology to computer vision. These models require significantly more computation than…
Generalized Boosting Algorithms for Convex Optimization
Alexander Grubb, J. Andrew Bagnell
Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks,…