Search Results for author: Luke Bornn

Found 13 papers, 4 papers with code

Inverse Bayesian Optimization: Learning Human Acquisition Functions in an Exploration vs Exploitation Search Task

1 code implementation16 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.

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

no code implementations18 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.

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

1 code implementation20 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.

Decision Making

PassNet: Learning pass probability surfaces from single-location labels. An architecture for visually-interpretable soccer analytics

no code implementations25 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.

Decision Making Sports Analytics

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

no code implementations13 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.

Activity Prediction Point Processes

Learning Person Trajectory Representations for Team Activity Analysis

no code implementations3 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.

Classifying X-ray Binaries: A Probabilistic Approach

1 code implementation13 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.

Fast and optimal nonparametric sequential design for astronomical observations

no code implementations11 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.

Astronomy Experimental Design

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball

2 code implementations5 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.

Dimensionality Reduction

Sequential Monte Carlo Bandits

no code implementations4 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.

Multi-Armed Bandits

PAWL-Forced Simulated Tempering

no code implementations22 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.

Modeling Non-Stationary Processes Through Dimension Expansion

no code implementations11 Nov 2010 Luke Bornn, Gavin Shaddick, James V Zidek

In this paper, we propose a novel approach to modeling nonstationary spatial fields.

Methodology Applications

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