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Browsing MIT Open Access Articles by Author "Rudin, Cynthia"

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Browsing MIT Open Access Articles by Author "Rudin, Cynthia"

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  • McCormick, Tyler H.; Rudin, Cynthia; Madigan, David (Institute of Mathematical Statistics, 2012)
    We propose a statistical modeling technique, called the Hierarchical Association Rule Model (HARM), that predicts a patient’s possible future medical conditions given the patient’s current and past history of reported ...
  • Wu, Leon; Rudin, Cynthia; Kaiser, Gail; Anderson, Roger (Association for Computing Machinery (ACM), 2011)
    Ensuring reliability as the electrical grid morphs into the "smart grid" will require innovations in how we assess the state of the grid, for the purpose of proactive maintenance, rather than reactive maintenance; in the ...
  • Wu, Leon; Teravainen, Timothy; Kaiser, Gail; Anderson, Roger; Boulanger, Albert; Rudin, Cynthia (Institute of Electrical and Electronics Engineers, 2011-07)
    An important problem in reliability engineering is to predict the failure rate, that is, the frequency with which an engineered system or component fails. This paper presents a new method of estimating failure rate using ...
  • Tulabandhula, Theja; Rudin, Cynthia (2012-01)
    This work concerns the way that statistical models are used to make decisions. In particular, we aim to merge the way estimation algorithms are designed with how they are used for a subsequent task. Our methodology considers ...
  • Kim, Been; Rudin, Cynthia (Springer, 2014-02)
    Most people participate in meetings almost every day, multiple times a day. The study of meetings is important, but also challenging, as it requires an understanding of social signals and complex interpersonal dynamics. ...
  • Rudin, Cynthia; Letham, Benjamin; Madigan, David (Association for Computing Machinery (ACM), 2013-11)
    We present a theoretical analysis for prediction algorithms based on association rules. As part of this analysis, we introduce a problem for which rules are particularly natural, called “sequential event prediction." In ...
  • Wang, Tong; Rudin, Cynthia; Wagner, Daniel; Sevieri, Rich (European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2013, 2013-08-21)
    Our goal is to automatically detect patterns of crime. Among a large set of crimes that happen every year in a major city, it is challenging, time-consuming, and labor-intensive for crime analysts to determine which ...
  • Tulabandhula, Theja; Rudin, Cynthia; Jaillet, Patrick (Springer Berlin / Heidelberg, 2011-10)
    The goal of the Machine Learning and Traveling Repairman Problem (ML&TRP) is to determine a route for a “repair crew,” which repairs nodes on a graph. The repair crew aims to minimize the cost of failures at the nodes, but ...
  • Rudin, Cynthia; Waltz, David; Anderson, Roger N.; Boulanger, Albert; Salleb-Aouissi, Ansaf; Chow, Maggie; Dutta, Haimonti; Gross, Philip N.; Huang, Bert; Ierome, Steve; Isaac, Delfina F.; Kressner, Arthur; Passonneau, Rebecca J.; Radeva, Axinia; Wu, Leon (Institute of Electrical and Electronics Engineers, 2012-02)
    Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into ...
  • Rudin, Cynthia; Tulabandhula, Theja (Association for Computing Machinery (ACM), 2013-07)
    This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the ...
  • Rudin, Cynthia; Schapire, Robert E. (MIT Press, 2009-10)
    We study boosting algorithms for learning to rank. We give a general margin-based bound for ranking based on covering numbers for the hypothesis space. Our bound suggests that algorithms that maximize the ranking margin ...
  • Ertekin, Seyda; Rudin, Cynthia (Association for Computing Machinery, 2011-10)
    We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from ...
  • Rudin, Cynthia; Ertekin, Seyda (MIT Press, 2011-10)
    We demonstrate that there are machine learning algorithms that can achieve success for two separate tasks simultaneously, namely the tasks of classification and bipartite ranking. This means that advantages gained from ...
  • Rudin, Cynthia (MIT Press, 2009-10)
    We are interested in supervised ranking algorithms that perform especially well near the top of the ranked list, and are only required to perform sufficiently well on the rest of the list. In this work, we provide a ...
  • Isaac, Delfina; Ierome, Steve; Dutta, Haimonti; Radeva, Axinia; Passonneau, Rebecca J.; Rudin, Cynthia (Springer Netherlands, 2010-07)
    We present a knowledge discovery and data mining process developed as part of the Columbia/Con Edison project on manhole event prediction. This process can assist with real-world prioritization problems that involve raw ...
  • Rudin, Cynthia; Mukherjee, Indraneel; Schapire, Robert E. (Association for Computing Machinery (ACM), 2013-08)
    The AdaBoost algorithm was designed to combine many “weak” hypotheses that perform slightly better than random guessing into a “strong” hypothesis that has very low error. We study the rate at which AdaBoost iteratively ...
  • Radeva, Axinia; Rudin, Cynthia; Passonneau, Rebecca; Isaac, Delfinia (Institute of Electrical and Electronics Engineers, 2010-01)
    We present a manhole profiling tool, developed as part of the Columbia/Con Edison machine learning project on manhole event prediction, and discuss its role in evaluating our machine learning model in three important ways: ...
  • Letham, Benjamin; Rudin, Cynthia; Madigan, David (Springer Science+Business Media, 2013-06)
    In sequential event prediction, we are given a “sequence database” of past event sequences to learn from, and we aim to predict the next event within a current event sequence. We focus on applications where the set of the ...
  • Rudin, Cynthia; Letham, Benjamin; Salleb-Aouissi, Ansaf; Kogan, Eugene; Madigan, David (Omnipress, 2011-07)
    We consider a supervised learning problem in which data are revealed sequentially and the goal is to determine what will next be revealed. In the context of this problem, algorithms based on association rules have a ...
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