b'Luke Bornn'

A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions

The expected possession value (EPV) of a soccer possession represents th...
11/18/2020 ∙ by Javier Fernandez, et al. ∙ 0 ∙ share

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SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer

We present a fully convolutional neural network architecture that is cap...
10/20/2020 ∙ by Javier Fernandez, et al. ∙ 0 ∙ share

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Estimating locomotor demands during team play from broadcast-derived tracking data

The introduction of optical tracking data across sports has given rise t...
01/21/2020 ∙ by Jacob Mortensen, et al. ∙ 0 ∙ share

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Measuring Spatial Allocative Efficiency in Basketball

Every shot in basketball has an opportunity cost; one player's shot elim...
12/11/2019 ∙ by Nathan Sandholtz, et al. ∙ 0 ∙ share

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Home Sweet Home: Quantifying Home Court Advantages For NCAA Basketball Statistics

Box score statistics are the baseline measures of performance for Nation...
09/11/2019 ∙ by Matthew van Bommel, et al. ∙ 0 ∙ share

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Using In-Game Shot Trajectories to Better Understand Defensive Impact in the NBA

As 3-point shooting in the NBA continues to increase, the importance of ...
05/02/2019 ∙ by Luke Bornn, et al. ∙ 0 ∙ share

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Winning Is Not Everything: A contextual analysis of hockey face-offs

This paper takes a different approach to evaluating face-offs in ice hoc...
02/06/2019 ∙ by Nick Czuzoj-Shulman, et al. ∙ 0 ∙ share

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Playing Fast Not Loose: Evaluating team-level pace of play in ice hockey using spatio-temporal possession data

Pace of play is an important characteristic in hockey as well as other t...
02/06/2019 ∙ by David Yu, et al. ∙ 0 ∙ share

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Transition Tensor Markov Decision Processes: Analyzing Shot Policies in Professional Basketball

In this paper we model basketball plays as episodes from team-specific n...
12/12/2018 ∙ by Nathan Sandholtz, et al. ∙ 0 ∙ share

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Rao-Blackwellizing Field Goal Percentage

Shooting skill in the NBA is typically measured by field goal percentage...
08/14/2018 ∙ by Daniel Daly-Grafstein, et al. ∙ 0 ∙ share

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Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction

Numerous powerful point process models have been developed to understand...
08/13/2018 ∙ by Yatao Zhong, et al. ∙ 0 ∙ share

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Time Perception Machine: Temporal PointProcesses for the When, Where and What ofActivity Prediction

Numerous powerful point process models have been developed to understand...
08/13/2018 ∙ by Yatao Zhong, et al. ∙ 0 ∙ share

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A Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs: Do School Districts Affect NYC House Prices?

Most research on regression discontinuity designs (RDDs) has focused on ...
07/11/2018 ∙ by Maxime Rischard, et al. ∙ 0 ∙ share

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Learning Person Trajectory Representations for Team Activity Analysis

Activity analysis in which multiple people interact across a large space...
06/03/2017 ∙ by Nazanin Mehrasa, et al. ∙ 0 ∙ share

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Fast and optimal nonparametric sequential design for astronomical observations

The spectral energy distribution (SED) is a relatively easy way for astr...
01/11/2015 ∙ by Justin J. Yang, et al. ∙ 0 ∙ share

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Diversifying Sparsity Using Variational Determinantal Point Processes

We propose a novel diverse feature selection method based on determinant...
11/23/2014 ∙ by Nematollah Kayhan Batmanghelich, et al. ∙ 0 ∙ share

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Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball

We develop a machine learning approach to represent and analyze the unde...
01/05/2014 ∙ by Andrew Miller, et al. ∙ 0 ∙ share

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Sequential Monte Carlo Bandits

In this paper we propose a flexible and efficient framework for handling...
10/04/2013 ∙ by Michael Cherkassky, et al. ∙ 0 ∙ share

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PAWL-Forced Simulated Tempering

In this short note, we show how the parallel adaptive Wang-Landau (PAWL)...
05/22/2013 ∙ by Luke Bornn, et al. ∙ 0 ∙ share

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Herded Gibbs Sampling

The Gibbs sampler is one of the most popular algorithms for inference in...
01/17/2013 ∙ by Luke Bornn, et al. ∙ 0 ∙ share

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Sparsity-Promoting Bayesian Dynamic Linear Models

Sparsity-promoting priors have become increasingly popular over recent y...
03/01/2012 ∙ by François Caron, et al. ∙ 0 ∙ share

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Discussion of "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by M. Girolami and B. Calderhead

This technical report is the union of two contributions to the discussio...
10/30/2010 ∙ by Luke Bornn, et al. ∙ 0 ∙ share

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Luke Bornn

Featured Co-authors

Max Welling

Nando de Freitas

Arnaud Doucet

Greg Mori

Polina Golland

Pavlos Protopapas

Yutian Chen

François Caron

Andrew Miller

Gareth W. Peters

Zach Branson

A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions

SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer

Estimating locomotor demands during team play from broadcast-derived tracking data

Measuring Spatial Allocative Efficiency in Basketball

Home Sweet Home: Quantifying Home Court Advantages For NCAA Basketball Statistics

Using In-Game Shot Trajectories to Better Understand Defensive Impact in the NBA

Winning Is Not Everything: A contextual analysis of hockey face-offs

Playing Fast Not Loose: Evaluating team-level pace of play in ice hockey using spatio-temporal possession data

Transition Tensor Markov Decision Processes: Analyzing Shot Policies in Professional Basketball

Rao-Blackwellizing Field Goal Percentage

Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction

Time Perception Machine: Temporal PointProcesses for the When, Where and What ofActivity Prediction

A Bayesian Nonparametric Approach to Geographic Regression Discontinuity Designs: Do School Districts Affect NYC House Prices?

Learning Person Trajectory Representations for Team Activity Analysis

Fast and optimal nonparametric sequential design for astronomical observations

Diversifying Sparsity Using Variational Determinantal Point Processes

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball

Sequential Monte Carlo Bandits

PAWL-Forced Simulated Tempering

Herded Gibbs Sampling

Sparsity-Promoting Bayesian Dynamic Linear Models

Discussion of "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by M. Girolami and B. Calderhead