Benjamin BloemReddy
I am a Postdoctoral Research Assistant in Statistical Machine Learning in the CSML group, based in the Department of Statistics at the University of Oxford.
Along with Jessie Wu, I organize the CSML Seminar, held on Friday afternoons during term time. See the seminar website for more information.
My research focuses on probabilistic and statistical analysis of discrete data. In particular, I work on probabilistic models, estimation, and inference for objects like graphs, partitions, and permutations. Natural applications of these ideas arise in, for example, modeling networks or text, and in matrix factorization. Recently, I have also worked on probabilistic programming, particularly in the context of Bayesian nonparametric models. I am generally interested in all aspects of machine learning, both theoretical and applied.
Previously, I completed my Ph.D. in Statistics at Columbia University, where I was advised by Peter Orbanz. I completed my B.S. in Physics at Stanford University, and my M.S. in Physics at Northwestern University, where I worked in the lab of William P. Halperin. Prior to studying at Columbia, I worked for three years as a research analyst at The Brattle Group in Washington, D.C.
Contact: benjamin dot bloemreddy at stats.ox.ac.uk
Office: Department of Statistics, Room 1.06
Papers

Sampling and Inference for Beta NeutraltotheLeft Models of Sparse Networks.
B. BloemReddy, A. Foster, E. Mathieu, Y. W. Teh
To appear, UAI 2018.

Random walk models of networks formation and sequential Monte Carlo methods for graphs.
B. BloemReddy and P. Orbanz
[arxiv] [A talk on this work] [Another talk on this work, by Peter Orbanz] 
Slice Sampling on Hamiltonian Trajectories.
B. BloemReddy and J.P. Cunningham
ICML 2016: JMLR W+CP.
[pdf] [icml/jmlr] [A talk on this work] 
Sampling and inference for discrete random probability measures in probabilistic programs.
B. BloemReddy*, E. Mathieu*, A. Foster, T. Rainforth, Y. W. Teh, M. Lomeli, H. Ge, Z. Ghahramani
NIPS 2017 Workshop on Advances in Approximate Bayesian Inference
[pdf] [poster] 
Random walk models of sparse graphs and networks.
B. Reddy and P. Orbanz
NIPS 2014 Workshop on Networks: From Graphs to Rich Data. Best student poster award.

Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures."
B. BloemReddy
Journal of the Royal Statistical Society, Series B, 79, Part 5.
[pdf] [slides from discussion at RSS meeting]
Workshop contributions
Other
Selected talks

Preferential attachment and vertex arrival times.
10th International Conference of the ERCIM WG on Computational and Methodological Statistics. London. 17 December 2017.

Preferential attachment, vertex arrival times, and probabilistic symmetries in random graphs.
University of Bristol Statistics Seminar. Bristol, UK. 13 October 2017.

Nested urn models of random partitions and graphs.
11th Conference on Bayesian Nonparametrics. Paris. June 2017.

Random walk models of networks: modeling and inferring complex dependence.
Workshop on Bayesian Methods for Networks. Isaac Newton Institute, Cambridge, UK. July 2016.
[video]

Slice Sampling on Hamiltonian Trajectories.
ICML. New York. June 2016.
[video]