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.
My research focuses on probabilistic and statistical analysis of discrete data. In particular, I have worked on probabilistic models 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 incorporating probabilistic symmetry into neural networks, and 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

Probabilistic symmetry and invariant neural networks
B. BloemReddy and Y. W. Teh
[arxiv] [pdf] [slides from a talk on this work]
(Neural network models of exchangeable sequences contains much of the material from Section 7.)

Sampling and Inference for Beta NeutraltotheLeft Models of Sparse Networks
B. BloemReddy, A. Foster, E. Mathieu, Y. W. Teh
UAI 2018
[uai] [arxiv] [code] 
Preferential Attachment and Vertex Arrival Times
B. BloemReddy and P. Orbanz
[arxiv]
(Sampling and Inference for Beta NeutraltotheLeft Models of Sparse Networks develops inference methods and applies these models to data.) 
Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures"
B. BloemReddy
Journal of the Royal Statistical Society, Series B (Statistical Methodology), 79(5)
[jrss b] [pdf] [slides from discussion at RSS meeting] 
Randomwalk models of networks formation and sequential Monte Carlo methods for graphs
B. BloemReddy and P. Orbanz
Journal of the Royal Statistical Society, Series B (Statistical Methodology), 80(5): 871898
[jrss b] [arxiv] [code]
[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] 
Superfluid Phase Stability of ^{3}He in Axially Anisotropic Aerogel
J. Pollanen, J. P. Davis, B. Reddy, K. R. Shirer, H. Choi, W. P. Halperin
Journal of Physics: Conference Series, 150(3), 032084
[iop] 
Stability of the axial phase of superfluid ^{3}He in aerogel with globally anisotropic scattering
J. P. Davis, J. Pollanen, B. Reddy, K. R. Shirer, H. Choi, W. P. Halperin
Physical Review B 77, 140502(R)
[aps] [arxiv] 
Neural network models of exchangeable sequences
B. BloemReddy and Y. W. Teh
NeurIPS 2018 Workshop on Bayesian Deep Learning
[pdf] [slides from a talk on this work] 
Sequential sampling of Gaussian process latent variable models
M. Tegner, B. BloemReddy, S. Roberts
ICML 2018 Workshop on Tractable Probabilistic Models
[arxiv] 
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
NeurIPS 2017 Workshop on Advances in Approximate Bayesian Inference
[pdf] [poster] 
Random walk models of sparse graphs and networks
B. Reddy and P. Orbanz
NeurIPS 2014 Workshop on Networks: From Graphs to Rich Data. Best student poster award.

Exchangeable random partitions and random discrete probability measures: a brief tour guided by the Dirichlet Process
B. BloemReddy
Notes for a lecture given to Oxford PhD students (these are a work in progress)
[pdf]