Sequential Monte Carlo Bandits. - Michael Cherkassky; Luke Bornn (CoRR, 2013)

Herded Gibbs Sampling - Luke Bornn; Yutian Chen 001; Nando de Freitas; Mareija Eskelin; Jing Fang; Max Welling (ICLR, 2013)

Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball. - Andrew C. Miller; Luke Bornn; Ryan P. Adams; Kirk Goldsberry (ICML, 2014)

Diversifying Sparsity Using Variational Determinantal Point Processes. - Nematollah Kayhan Batmanghelich; Gerald T. Quon; Alex Kulesza; Manolis Kellis; Polina Golland; Luke Bornn (CoRR, 2014)

Learning Person Trajectory Representations for Team Activity Analysis. - Nazanin Mehrasa; Yatao Zhong; Frederick Tung; Luke Bornn; Greg Mori (CoRR, 2017)

Adjusting for scorekeeper bias in NBA box scores. - Matthew van Bommel; Luke Bornn (Data Min. Knowl. Discov., 2017)

Improving simulated annealing through derandomization. - Mathieu Gerber; Luke Bornn (J. Glob. Optim., 2017)

Modeling offensive player movement in professional basketball. - Steven Wu; Luke Bornn (PeerJ Prepr., 2017)

The use of a single pseudo-sample in approximate Bayesian computation. - Luke Bornn; Natesh S. Pillai; Aaron Smith; Dawn Woodard (Stat. Comput., 2017)

Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction. - Yatao Zhong; Bicheng Xu; Guang-Tong Zhou; Luke Bornn; Greg Mori (CoRR, 2018)

SoccerMap: A Deep Learning Architecture for Visually-Interpretable Analysis in Soccer. - Javier Fernández 006; Luke Bornn (ECML/PKDD (5), 2020)

Inverse Bayesian Optimization: Learning Human Search Strategies in a Sequential Optimization Task. - Nathan Sandholtz; Yohsuke Miyamoto; Luke Bornn; Maurice Smith (CoRR, 2021)

A framework for the fine-grained evaluation of the instantaneous expected value of soccer possessions. - Javier Fernández 006; Luke Bornn; Daniel Cervone (Mach. Learn., 2021)