Publications

2022
Article

Nonparametric Subset Scanning for Detection of Heteroscedasticity

By: Charles R. Doss and Edward McFowland III

We propose Heteroscedastic Subset Scan (HSS), a novel method for identifying covariates that are responsible for violations of the homoscedasticity assumption in regression settings. Viewing the problem as one of anomalous pattern detection, we use subset scanning... View Details

Keywords: Scan Statistics; Anomaly Detection; Regression; Model Diagnostics

Article

Pattern Detection in the Activation Space for Identifying Synthesized Content

By: Celia Cintas, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III and Komminist Weldemariam

Generative Adversarial Networks (GANs) have recently achieved unprecedented success in photo-realistic image synthesis from low-dimensional random noise. The ability to synthesize high-quality content at a large scale brings potential risks as the generated samples may... View Details

Keywords: Subset Scanning; Generative Models; Synthetic Content Detection

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Article

Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error

By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III

Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details

Keywords: Autoencoder Networks; Pattern Detection; Subset Scanning; Computer Vision; Statistical Methods And Machine Learning; Machine Learning; Deep Learning; Data Mining; Big Data; Large-scale Systems; Mathematical Methods; Analytics and Data Science

November 2021
Article

Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data

By: William Herlands, Edward McFowland III, Andrew Gordon Wilson and Daniel B. Neill

Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle,... View Details

Keywords: Pattern Detection; Subset Scanning; Gaussian Processes; Mathematical Methods

2023
Working Paper

Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection

By: Edward McFowland III, Sriram Somanchi and Daniel B. Neill

In the recent literature on estimating heterogeneous treatment effects, each proposed method makes its own set of restrictive assumptions about the intervention’s effects and which subpopulations to explicitly estimate. Moreover, the majority of the literature provides... View Details

Keywords: Causal Inference; Program Evaluation; Algorithms; Distributional Average Treatment Effect; Treatment Effect Subset Scan; Heterogeneous Treatment Effects

2016
Article

Penalized Fast Subset Scanning

By: Skyler Speakman, Sriram Somanchi, Edward McFowland III and Daniel B. Neill

We present the penalized fast subset scan (PFSS), a new and general framework for scalable and accurate pattern detection. PFSS enables exact and efficient identification of the most anomalous subsets of the data, as measured by a likelihood ratio scan statistic.... View Details

Keywords: Disease Surveillance; Likelihood Ratio Statistic; Pattern Detection; Scan Statistic; Mathematical Methods

2015
Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details

Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic

Article

Fast Generalized Subset Scan for Anomalous Pattern Detection

By: Edward McFowland III, Skyler Speakman and Daniel B. Neill

We propose Fast Generalized Subset Scan (FGSS), a new method for detecting anomalous patterns in general categorical data sets. We frame the pattern detection problem as a search over subsets of data records and attributes, maximizing a nonparametric scan statistic... View Details

Keywords: Pattern Detection; Anomaly Detection; Knowledge Discovery; Bayesian Networks; Scan Statistics; Analytics and Data Science

Article

Fast Subset Scan for Multivariate Spatial Biosurveillance

By: Daniel B. Neill, Edward McFowland III and Huanian Zheng

We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time... View Details

Keywords: Algorithms; Disease Surveillance; Event Detection; Scan Statistics; Spatial Scan

2011
Article

Scalable Detection of Anomalous Patterns With Connectivity Constraints

By: Skyler Speakman, Edward McFowland III and Daniel B. Neill

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters in graph or network data. Given a graph structure, data observed at each node, and a score function defining the anomalousness of a set of nodes, GraphScan can efficiently and... View Details

Keywords: Biosurveillance; Event Detection; Graph Mining; Scan Statistics; Spatial Scan Statistic

Article

Fast Subset Scan for Multivariate Spatial Biosurveillance

By: Daniel B. Neill, Edward McFowland III and Huanian Zheng

We extend the recently proposed ‘fast subset scan’ framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time clusters even when the numbers of spatial locations and data streams are large. These fast algorithms... View Details