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  • All HBS Web  (82)
    • Faculty Publications  (5)

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    • All HBS Web  (82)
      • Faculty Publications  (5)

      Accuracy First Remove Accuracy First →

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      • Article

      Oracle Efficient Private Non-Convex Optimization

      By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
      One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it....  View Details
      Keywords: Machine Learning; Algorithms; Objective Perturbation; Mathematical Methods
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      Neel, Seth, Aaron Leon Roth, Giuseppe Vietri, and Zhiwei Steven Wu. "Oracle Efficient Private Non-Convex Optimization." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020).
      • Article

      Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM

      By: Katrina Ligett, Seth Neel, Aaron Leon Roth, Bo Waggoner and Steven Wu
      Traditional approaches to differential privacy assume a fixed privacy requirement ϵ for a computation, and attempt to maximize the accuracy of the computation subject to the privacy constraint. As differential privacy is increasingly deployed in practical settings, it...  View Details
      Keywords: Differential Privacy; Empirical Risk Minimization; Accuracy First
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      Ligett, Katrina, Seth Neel, Aaron Leon Roth, Bo Waggoner, and Steven Wu. "Accuracy First: Selecting a Differential Privacy Level for Accuracy-Constrained ERM." Journal of Privacy and Confidentiality 9, no. 2 (2019).
      • 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.
      • January 2008 (Revised July 2009)
      • Case

      Forecasting the Great Depression

      By: Walter A. Friedman
      What is proper role of professional economic forecasting in financial decision making? The case presents excerpts from three leading economic forecasters on the eve of, and just after, the stock market crash of October 1929. The first set of excerpts is from Roger...  View Details
      Keywords: History; Mathematical Methods; Personal Development and Career; Forecasting and Prediction; Financial Crisis
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      Friedman, Walter A. "Forecasting the Great Depression." Harvard Business School Case 708-046, January 2008. (Revised July 2009.)
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