Google Scholar is probably more up-to-date, but here are some papers I’ve worked on. In general, we order author names alphabetically.
Publications
- Global-local mixtures. Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, and Brandon Willard. q, pages 1–22, February 2020. [URL].
- Lasso Meets Horseshoe. Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, and Brandon T. Willard. Statistical Science, 34(3):405–427, 2019. [URL], arXiv:1706.10179.
- Horseshoe regularization for feature subset selection. Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, and Brandon Willard. Sankhya B, pages 1–30, December 2019. [URL], arXiv:1702.07400.
- Prediction risk for horseshoe regression. Anindya Bhadra, Jyotishka Datta, Yunfan Li, Nicholas G. Polson, and Brandon Willard. Journal of Machine Learning Research, 20(78):1–39, 2019. [URL].
- Default bayesian analysis with global-local shrinkage priors. Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, and Brandon Willard. Biometrika, 103(4):955–969, December 2016.
- The horseshoe+ estimator of ultra-sparse signals. Anindya Bhadra, Jyotishka Datta, Nicholas G. Polson, and Brandon Willard. Bayesian Analysis, 2016.
- A Statistical Theory of Deep Learning via Proximal Splitting. Nicholas G. Polson, Brandon T. Willard, and Massoud Heidari. arXiv preprint arXiv:1509.06061, 2015. [URL], arXiv:1509.06061.
- Proximal algorithms in statistics and machine learning. Nicholas G. Polson, James G. Scott, and Brandon T. Willard. Statistical Science, 30(4):559–581, November 2015.
- Real-time On and Off Road GPS Tracking. Brandon T. Willard. arXiv preprint arXiv:1303.1883, 2013. [URL], arXiv:1303.1883.
Reports
- Recursive Bayesian Computation of the Dynamic Logit Model. Brandon T. Willard. Technical Report, University of Chicago, December 2014. [URL].
- Functions Alternative to the Likelihood. Brandon T. Willard. Technical Report, Cornell, Wayne State, 2007. [URL].
- Computation of NRQCD Parameters in Potential Models. Brandon Willard. Technical Report, Cornell, Wayne State, 2004. [URL].