71 citations · 76 across the 5 of their papers we have counts for
8 papers
Model-agnostic clean-label backdoor mitigation in cybersecurity environments
Giorgio Severi, Simona Boboila, John Holodnak +4
The training phase of machine learning models is a delicate step, especially in cybersecurity contexts. Recent research has surfaced a series of insidious training-time attacks tha…
ScatterUQ: Interactive Uncertainty Visualizations for Multiclass Deep Learning Problems
Harry Li, Steven Jorgensen, John Holodnak +1
Recently, uncertainty-aware deep learning methods for multiclass labeling problems have been developed that provide calibrated class prediction probabilities and out-of-distributio…
Poisoning Network Flow Classifiers
Giorgio Severi, Simona Boboila, Alina Oprea +3
As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes critical. This…
Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification
Steven Jorgensen, John Holodnak, Jensen Dempsey +6
With the increasing prevalence of encrypted network traffic, cyber security analysts have been turning to machine learning (ML) techniques to elucidate the traffic on their network…
On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks
John T. Holodnak, Allan B. Wollaber
Multiple techniques for producing calibrated predictive probabilities using deep neural networks in supervised learning settings have emerged that leverage approaches to ensemble d…
A Probabilistic Subspace Bound with Application to Active Subspaces
John T. Holodnak, Ilse C. F. Ipsen, Ralph C. Smith
Given a real symmetric positive semi-definite matrix E, and an approximation S that is a sum of n independent matrix-valued random variables, we present bounds on the relative erro…
Conditioning of Leverage Scores and Computation by QR Decomposition
John T. Holodnak, Ilse C. F. Ipsen, Thomas A. Wentworth
The leverage scores of a full-column rank matrix A are the squared row norms of any orthonormal basis for range(A). We show that corresponding leverage scores of two matrices A and…
Randomized Approximation of the Gram Matrix: Exact Computation and Probabilistic Bounds
John T. Holodnak, Ilse C. F. Ipsen
Given a real matrix A with n columns, the problem is to approximate the Gram product AA^T by c << n weighted outer products of columns of A. Necessary and sufficient conditions for…