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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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  • Rudin, Cynthia; Passonneau, Rebecca; Radeva, Axinia; Tomar, Ashish; Xie, Boyi; Lewis, Stanley; Riddle, Mark; Pangsrivinij, Debbie; McCormick, Tyler; Ertekin, Seyda (Institute for Operations Research and the Management Sciences (INFORMS), 2014-06)
    We summarize the first major effort to use analytics for preemptive maintenance and repair of an electrical distribution network. This is a large-scale multiyear effort between scientists and students at Columbia University ...
  • Kim, Been; Rudin, Cynthia; Shah, Julie A. (Neural Information Processing Systems Foundation, Inc., 2014-12)
    We present the Bayesian Case Model (BCM), a general framework for Bayesian case-based reasoning (CBR) and prototype classification and clustering. BCM brings the intuitive power of CBR to a Bayesian generative framework. ...
  • 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 ...
  • Goh, Siong Thye; Rudin, Cynthia (Association for Computing Machinery (ACM), 2014-08)
    The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms ...
  • 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 ...
  • Letham, Benjamin; Rudin, Cynthia; Heller, Katherine A. (Springer-Verlag, 2013-07)
    It is easy to find expert knowledge on the Internet on almost any topic, but obtaining a complete overview of a given topic is not always easy: information can be scattered across many sources and must be aggregated to be ...
  • 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 ...
  • Wang, Dingquan; Passonneau, Rebecca J.; Collins, Michael; Rudin, Cynthia (Elsevier, 2014-06)
    Weather can cause problems for underground electrical grids by increasing the probability of serious “manhole events” such as fires and explosions. In this work, we compare a model that incorporates weather features ...
  • 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 ...
  • 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 (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 F.; 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 ...
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