33.1k citations · 33.1k across the 4 of their papers we have counts for
4 papers
Reducing Catastrophic Risk from AI with Systematic Monitoring and Evaluation of Rogue AI Progression
T. Bauer, W. P. Kegelmeyer, E. Begoli +15
This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artificial intelligence systems. We a…
Mind the Gap: On Bridging the Semantic Gap between Machine Learning and Information Security
Michael R. Smith, Nicholas T. Johnson, Joe B. Ingram +6
Despite the potential of Machine learning (ML) to learn the behavior of malware, detect novel malware samples, and significantly improve information security (InfoSec) we see few,…
SMOTE: Synthetic Minority Over-sampling Technique
N. V. Chawla, K. W. Bowyer, L. O. Hall +1
An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represe…
COMET: A Recipe for Learning and Using Large Ensembles on Massive Data
Justin D. Basilico, M. Arthur Munson, Tamara G. Kolda +2
COMET is a single-pass MapReduce algorithm for learning on large-scale data. It builds multiple random forest ensembles on distributed blocks of data and merges them into a mega-en…