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Bornn, Luke

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Bornn, Luke

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2014 - 20162

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Author's Publications: search.filters.isAuthorOfPublication.33a5edfa-971e-41f5-aed8-7251d4975d20

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Now showing 1 - 2 of 2
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    Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
    (Journal of Machine Learning Research, 2014) Miller, Andrew; Bornn, Luke; Adams, Ryan Prescott; Goldsberry, Kirk P
    We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. This makes it difficult to draw comparisons between players and make accurate player specific predictions. Modeling shot attempt data as a point process, we create a low dimensional representation of offensive player types in the NBA. Using non-negative matrix factorization (NMF), an unsupervised dimensionality reduction technique, we show that a low-rank spatial decomposition summarizes the shooting habits of NBA players. The spatial representations discovered by the algorithm correspond to intuitive descriptions of NBA player types, and can be used to model other spatial effects, such as shooting accuracy.
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    The use of a single pseudo-sample in approximate Bayesian computation
    (Springer Nature, 2016) Bornn, Luke; Pillai, Natesh; Smith, Aaron; Woodard, Dawn
    We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For the rejection sampling version of ABC, it is known that multiple pseudo-samples cannot substantially increase (and can substantially decrease) the efficiency of the algorithm as compared to employing a high-variance estimate based on a single pseudo-sample. We show that this conclusion also holds for a Markov chain Monte Carlo version of ABC, implying that it is unnecessary to tune the number of pseudo-samples used in ABC-MCMC. This conclusion is in contrast to particle MCMC methods, for which increasing the number of particles can provide large gains in computational efficiency.