Justin Doak
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View article: Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions.
Preliminary Results for Using Uncertainty and Out-of-distribution Detection to Identify Unreliable Predictions. Open
As machine learning (ML) models are deployed into an ever-diversifying set of application spaces, ranging from self-driving cars to cybersecurity to climate modeling, the need to carefully evaluate model credibility becomes increasingly im…
View article: Decision Science for Machine Learning (DeSciML)
Decision Science for Machine Learning (DeSciML) Open
The increasing use of machine learning (ML) models to support high-consequence decision making drives a need to increase the rigor of ML-based decision making. Critical problems ranging from climate change to nonproliferation monitoring re…
View article: Optimizing Machine Learning Decisions with Prediction Uncertainty.
Optimizing Machine Learning Decisions with Prediction Uncertainty. Open
these classifiers can be uncertain, rendering downstream decisions difficult. Herein, we provide a framework for: (1) quantifying and propagating uncertainty in ML classifiers; (2) formally linking ML outputs with the decision-making proce…
View article: Temporal Cyber Attack Detection.
Temporal Cyber Attack Detection. Open
Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack…
View article: Learning to rank for alert triage
Learning to rank for alert triage Open
As cyber monitoring capabilities expand and data rates increase, cyber security analysts must filter through an increasing number of alerts in order to identify potential intrusions on the network. This process is often manual and time-con…