149 citations · 529 across the 35 of their papers we have counts for
5 papers · 1 filter
Diversifying Sparsity Using Variational Determinantal Point Processes
Nematollah Kayhan Batmanghelich, Gerald Quon, Alex Kulesza +3
We propose a novel diverse feature selection method based on determinantal point processes (DPPs). Our model enables one to flexibly define diversity based on the covariance of fea…
A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes
Daniel Cervone, Alex D'Amour, Luke Bornn +1
Basketball games evolve continuously in space and time as players constantly interact with their teammates, the opposing team, and the ball. However, current analyses of basketball…
Characterizing the spatial structure of defensive skill in professional basketball
Alexander Franks, Andrew Miller, Luke Bornn +1
Although basketball is a dualistic sport, with all players competing on both offense and defense, almost all of the sport's conventional metrics are designed to summarize offensive…
The Use of a Single Pseudo-Sample in Approximate Bayesian Computation
Luke Bornn, Natesh Pillai, Aaron Smith +1
We analyze the computational efficiency of approximate Bayesian computation (ABC), which approximates a likelihood function by drawing pseudo-samples from the associated model. For…
Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball
Andrew Miller, Luke Bornn, Ryan Adams +1
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. Typic…