A process for predicting manhole events in Manhattan

By Cynthia Rudin, Rebecca J. Passonneau, Axinia Radeva, Haimonti Dutta, Steve Ierome, Delfina Isaac. 2010

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Signal Processing

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Citations in the corpus

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “erwhelmed by the ability to interpret results and process the data better at the next iteration. Efforts working within a knowledge discovery process led me to work in interpretable machine learning [Rudin et al., 2010]. This was a large-scale effort to predict electrical grid failures across New Yo”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “Efforts working within a knowledge discovery process led me to work in interpretable machine learning [Rudin et al., 2010].”
Reactive point processes: A new approach to predicting power failures in underground electrical systems (2015, methodology): “A multi-year, ongoing collaboration to predict these events in advance was started in 2007 [Rudin et al. (2010, 2012, 2014)], where diverse historical data were used to predict manhole events over a long-term horizon, as the data were not originally processed enough to predict events in the short…”
Machine learning for science and society (2013, background): “For instance, one of the editors of this special issue uses ML to predict manhole ﬁres and explosions on the NYC power grid (Rudin et al. 2010, 2012).”
On combining machine learning with decision making (2011, methodology): “The dataset we use is described by Rudin et al. (2010), which was developed in order to assist Con Edison with its maintenance and repair programs on the secondary electrical distribution network in NYC; speciﬁcally, it was designed for the purpose of predicting manhole ﬁres and explosions.”
Integer optimization methods for machine learning (2012, background): “For instance, a more accurate ranking of electrical grid components in New York City, in order of vulnerability to failures or dangerous events, helps prevent power outages as well as serious accidents from fires and explosions [Gross et al., 2009, Rudin et al., 2010].”
Direct Learning to Rank and Rerank (2018, methodology): “…equipment for repair (e.g., airplanes, as considered by Oza et al, 2009), it could be useful for prioritizing maintenance on the power grid (Rudin et al, 2012, 2010), it could be used for ranking medical workers in order of likelihood that they accessed medical records inappropriately (as…”
Machine learning with operational costs (2011, methodology): “…models have started to be used for proactive maintenance in NYC, where supervised ranking algorithms are used to rank the manholes in order of predicted susceptibility to failure (fires, explosions, smoke) so that the most vulnerable manholes can be prioritized (Rudin et al., 2010, 2011b,a).”