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Publications

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  • All HBS Web  (67)
    • News  (12)
    • Research  (55)
  • Faculty Publications  (16)

Show Results For

  • All HBS Web  (67)
    • News  (12)
    • Research  (55)
  • Faculty Publications  (16)
Page 1 of 67 Results →
  • 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
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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.
  • 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.)
  • 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.
  • 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

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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Cintas, Celia, Skyler Speakman, Girmaw Abebe Tadesse, Victor Akinwande, Edward McFowland III, and Komminist Weldemariam. "Pattern Detection in the Activation Space for Identifying Synthesized Content." Pattern Recognition Letters 153 (January 2022): 207–213.
  • 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.
  • 2021
  • Working Paper

Detecting Anomalous Patterns of Care Using Health Insurance Claims

By: Sriram Somanchi, Edward McFowland III and Daniel B. Neill
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Somanchi, Sriram, Edward McFowland III, and Daniel B. Neill. "Detecting Anomalous Patterns of Care Using Health Insurance Claims." Working Paper, 2021. (In Preparation.)
  • 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
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McFowland III, Edward, Sriram Somanchi, and Daniel B. Neill. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection." Working Paper, 2023.
  • 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).
  • Article

Multivoxel Patterns in Face-sensitive Temporal Regions Reveal an Encoding Schema Based on Detecting Life in a Face

By: Christine E. Looser, J. Swaroop Guntupalli and Thalia Wheatley
More than a decade of research has demonstrated that faces evoke prioritized processing in a 'core face network' of three brain regions. However, whether these regions prioritize the detection of global facial form (shared by humans and mannequins) or the detection of... View Details
Keywords: Brain Imaging; Social Psychology; Mind Perception; Identity; Science; Cognition and Thinking
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Looser, Christine E., J. Swaroop Guntupalli, and Thalia Wheatley. "Multivoxel Patterns in Face-sensitive Temporal Regions Reveal an Encoding Schema Based on Detecting Life in a Face." Social Cognitive and Affective Neuroscience 8, no. 7 (October 2013): 799–805.
  • January 2021
  • Article

Machine Learning for Pattern Discovery in Management Research

By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect... View Details
Keywords: Machine Learning; Supervised Machine Learning; Induction; Abduction; Exploratory Data Analysis; Pattern Discovery; Decision Trees; Random Forests; Neural Networks; ROC Curve; Confusion Matrix; Partial Dependence Plots; AI and Machine Learning
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Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Strategic Management Journal 42, no. 1 (January 2021): 30–57.
  • 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.)
  • May 2011
  • Article

Race at the Top: How Companies Shape the Inclusion of African Americans on Their Boards in Response to Institutional Pressures

By: Clayton S. Rose and William T. Bielby
Drawing on institutionalist theory, we conceptualize the racial composition of the boards of directors of large American companies as shaped in response to social and political norms. We use new longitudinal and cross-sectional data to test hypotheses about factors... View Details
Keywords: Leadership; Governing and Advisory Boards; Race; Mathematical Methods; Government and Politics; Public Ownership; United States
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Rose, Clayton S., and William T. Bielby. "Race at the Top: How Companies Shape the Inclusion of African Americans on Their Boards in Response to Institutional Pressures." Social Science Research 40, no. 3 (May 2011): 841–859.
  • 2023
  • Working Paper

Much Ado About Nothing? Overreaction to Random Regulatory Audits

By: Samuel Antill and Joseph Kalmenovitz
Regulators often audit firms to detect non-compliance. Exploiting a natural experiment in the lobbying industry, we show that firms overreact to audits and this response distorts prices and reduces welfare. Each year, federal regulators audit a random sample of... View Details
Keywords: Governance Compliance; Governing Rules, Regulations, and Reforms; Price
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Antill, Samuel, and Joseph Kalmenovitz. "Much Ado About Nothing? Overreaction to Random Regulatory Audits." Working Paper, August 2023.

    How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework

    This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call... View Details
    • November 2019
    • Article

    How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework

    By: Doug J. Chung, Byungyeon Kim and Byoung G. Park
    This paper evaluates the short- and long-term value of sales representatives’ detailing visits to different types of physicians. By understanding the dynamic effect of sales calls across heterogeneous physicians, we provide guidance on the design of optimal call... View Details
    Keywords: Nerlove-Arrow Framework; Stock-of-goodwill; Dynamic Panel Data; Serial Correlation; Instrumental Variables; Sales Effectiveness; Detailing; Analytics and Data Science; Sales; Analysis; Performance Effectiveness; Pharmaceutical Industry
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    Chung, Doug J., Byungyeon Kim, and Byoung G. Park. "How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove-Arrow Framework." Management Science 65, no. 11 (November 2019): 5197–5218.
    • 11 Feb 2013
    • Research & Ideas

    Neuroeconomics: Eyes, Brain, Business

    spurious face-like pattern in a rock than miss a predator," the researchers write in their paper "Multivoxel Patterns in Face-Sensitive Temporal Regions Reveal an Encoding Schema Based on View Details
    Keywords: by Carmen Nobel
    • 11 Feb 2010
    • Working Paper Summaries

    The Architecture of Complex Systems: Do Core-periphery Structures Dominate?

    Keywords: by Alan MacCormack, Carliss Y. Baldwin & John Rusnak; Video Game; Web Services
    • 22 May 2013
    • Working Paper Summaries

    Hidden Structure: Using Network Methods to Map System Architecture

    Keywords: by Carliss Y. Baldwin, Alan MacCormack & John Rusnak
    • 05 Nov 2013
    • First Look

    First Look: November 5

    and political scientists have written extensively on the history of globalization and patterns of global wealth and poverty, but business enterprises have rarely been identified as significant independent actors. This book argues that... View Details
    Keywords: Sean Silverthorne
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