1 code implementation • 16 Apr 2021 • Nathan Sandholtz, Yohsuke Miyamoto, Luke Bornn, Maurice Smith
This paper introduces a probabilistic framework to estimate parameters of an acquisition function given observed human behavior that can be modeled as a collection of sample paths from a Bayesian optimization procedure.
no code implementations • 18 Nov 2020 • Javier Fernandez, Luke Bornn, Daniel Cervone
The expected possession value (EPV) of a soccer possession represents the likelihood of a team scoring or receiving the next goal at any time instance.
1 code implementation • 20 Oct 2020 • Javier Fernández, Luke Bornn
We present a fully convolutional neural network architecture that is capable of estimating full probability surfaces of potential passes in soccer, derived from high-frequency spatiotemporal data.
no code implementations • 25 Sep 2019 • Javier Fernández, Luke Bornn
We propose a fully convolutional network architecture that is able to estimate a full surface of pass probabilities from single-location labels derived from high frequency spatio-temporal data of professional soccer matches.
no code implementations • 13 Aug 2018 • Yatao Zhong, Bicheng Xu, Guang-Tong Zhou, Luke Bornn, Greg Mori
Numerous powerful point process models have been developed to understand temporal patterns in sequential data from fields such as health-care, electronic commerce, social networks, and natural disaster forecasting.
no code implementations • 3 Jun 2017 • Nazanin Mehrasa, Yatao Zhong, Frederick Tung, Luke Bornn, Greg Mori
Activity analysis in which multiple people interact across a large space is challenging due to the interplay of individual actions and collective group dynamics.
1 code implementation • 13 Jul 2015 • Giri Gopalan, Saeqa Dil Vrtilek, Luke Bornn
We use this model to estimate the probabilities that an X-ray binary system contains a black hole, non-pulsing neutron star, or pulsing neutron star.
no code implementations • 11 Jan 2015 • Justin J. Yang, Xufei Wang, Pavlos Protopapas, Luke Bornn
The spectral energy distribution (SED) is a relatively easy way for astronomers to distinguish between different astronomical objects such as galaxies, black holes, and stellar objects.
no code implementations • 23 Nov 2014 • Nematollah Kayhan Batmanghelich, Gerald Quon, Alex Kulesza, Manolis Kellis, Polina Golland, Luke Bornn
We propose a novel diverse feature selection method based on determinantal point processes (DPPs).
2 code implementations • 5 Jan 2014 • Andrew Miller, Luke Bornn, Ryan Adams, Kirk Goldsberry
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.
no code implementations • 4 Oct 2013 • Michael Cherkassky, Luke Bornn
In this paper we propose a flexible and efficient framework for handling multi-armed bandits, combining sequential Monte Carlo algorithms with hierarchical Bayesian modeling techniques.
no code implementations • 22 May 2013 • Luke Bornn
In this short note, we show how the parallel adaptive Wang-Landau (PAWL) algorithm of Bornn et al. (2013) can be used to automate and improve simulated tempering algorithms.
no code implementations • 11 Nov 2010 • Luke Bornn, Gavin Shaddick, James V Zidek
In this paper, we propose a novel approach to modeling nonstationary spatial fields.
Methodology Applications