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Publications

Publications

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  • All HBS Web  (40)
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    • All HBS Web  (40)
      • Faculty Publications  (13)

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      • 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
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      Doss, Charles R., and Edward McFowland III. "Nonparametric Subset Scanning for Detection of Heteroscedasticity." Journal of Computational and Graphical Statistics 31, no. 3 (2022): 813–823.
      • 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
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      Cintas, Celia, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan, and Edward McFowland III. "Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error." Proceedings of the International Joint Conference on Artificial Intelligence 29th (2020).
      • 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
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      Herlands, William, Edward McFowland III, Andrew Gordon Wilson, and Daniel B. Neill. "Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data." Proceedings of Machine Learning Research (PMLR) 84 (2018): 425–434. (Also presented at the 21st International Conference on Artificial Intelligence and Statistics (AISTATS), 2018.)
      • 2018
      • Working Paper

      Efficient Discovery of Heterogeneous 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
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      McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2018. (2nd Round Revision.)
      • Article

      Statistical Physics of Human Cooperation

      By: Matjaž Perc, Jillian J. Jordan, David G. Rand, Zhen Wang, Stefano Boccaletti and Attila Szolnoki
      Extensive cooperation among unrelated individuals is unique to humans, who often sacrifice personal benefits for the common good and work together to achieve what they are unable to execute alone. The evolutionary success of our species is indeed due, to a large...  View Details
      Keywords: Human Cooperation; Evolutionary Game Theory; Public Goods; Reward; Punishment; Tolerance; Self-organization; Pattern Formation; Cooperation; Behavior; Game Theory
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      Perc, Matjaž, Jillian J. Jordan, David G. Rand, Zhen Wang, Stefano Boccaletti, and Attila Szolnoki. "Statistical Physics of Human Cooperation." Physics Reports 687 (May 8, 2017): 1–51.
      • May 2017
      • Article

      Agent-based Modeling: A Guide for Social Psychologists

      By: Joshua Conrad Jackson, David Rand, Kevin Lewis, Michael I. Norton and Kurt Gray
      Agent-based modeling is a longstanding but underused method that allows researchers to simulate artificial worlds for hypothesis testing and theory building. Agent-based models (ABMs) offer unprecedented control and statistical power by allowing researchers to...  View Details
      Keywords: Social Psychology; Marketing; Mathematical Methods
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      Jackson, Joshua Conrad, David Rand, Kevin Lewis, Michael I. Norton, and Kurt Gray. "Agent-based Modeling: A Guide for Social Psychologists." Social Psychological & Personality Science 8, no. 4 (May 2017): 387–395.
      • 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
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      Speakman, Skyler, Sriram Somanchi, Edward McFowland III, and Daniel B. Neill. "Penalized Fast Subset Scanning." Journal of Computational and Graphical Statistics 25, no. 2 (2016): 382–404. (Selected for “Best of JCGS” invited session by the journal’s editor in chief.)
      • 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
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      Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Journal of Computational and Graphical Statistics 24, no. 4 (2015): 1014–1033.
      • 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
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      McFowland III, Edward, Skyler Speakman, and Daniel B. Neill. "Fast Generalized Subset Scan for Anomalous Pattern Detection." Art. 12. Journal of Machine Learning Research 14 (2013): 1533–1561.
      • 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
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      Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Statistics in Medicine 32, no. 13 (June 15, 2013): 2185–2208.
      • 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
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      Speakman, Skyler, Edward McFowland III, and Daniel B. Neill. "Scalable Detection of Anomalous Patterns With Connectivity Constraints." Emerging Health Threats Journal 4 (2011): 11121.
      • 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
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      Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Emerging Health Threats Journal 4, Suppl. 1, no. s42 (2011).
      • November 1996
      • Article

      Localized Autocorrelation Diagnostic Statistic for Sociological Models: Times-series, Network, and Spatial Datasets

      By: C. I. Nass and Y. Moon
      Keywords: Society; Analytics and Data Science; Information
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      Nass, C. I., and Y. Moon. "Localized Autocorrelation Diagnostic Statistic for Sociological Models: Times-series, Network, and Spatial Datasets." Sociological Methods & Research 25, no. 2 (November 1996): 223–247.
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