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Outputs (4)

Model updating after interventions paradoxically introduces bias (2021)
Conference Proceeding
Liley, J., Emerson, S., Mateen, B., Vallejos, C., Aslett, L., & Vollmer, S. (in press). Model updating after interventions paradoxically introduces bias. In Proceedings of The 24th International Conference on Artificial Intelligence and Statistics (3916-3924)

Encrypted accelerated least squares regression (2017)
Conference Proceeding
Esperança, P., Aslett, L., & Holmes, C. (2017). Encrypted accelerated least squares regression. In A. Singh, & J. Zhu (Eds.), Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54 (334-343)

Information that is stored in an encrypted format is, by definition, usually not amenable to statistical analysis or machine learning methods. In this paper we present detailed analysis of coordinate and accelerated gradient descent algorithms which... Read More about Encrypted accelerated least squares regression.

Using Storm for scaleable sequential statistical inference. (2014)
Conference Proceeding
Wilson, S., Mai, T., Cogan, P., Bhattacharya, A., Robles-Sánchez, O., Aslett, L., …Roetzer, G. (2014). Using Storm for scaleable sequential statistical inference. In M. Gilli, G. González-Rodríguez, & A. Nieto-Reyes (Eds.), Proceedings of COMPSTAT 2014: 21st International Conference on Computational Statistics (hosting the 5th IASC World Conference): Geneva, Switzerland, August 19–22, 2014 (103-109)

This article describes Storm, an environment for doing streaming data analysis. Two examples of sequential data analysis — computation of a running summary statistic and sequential updating of a posterior distribution — are implemented and their perf... Read More about Using Storm for scaleable sequential statistical inference..