NEWS: Along with Brooks Paige, Matt J. Kusner, Rich Caruana, Tom Rainforth, and Yee Whye Teh, I am organizing a NIPS 2018 workshop: Critiquing and Correcting Trends in Machine Learning. If you'll be at NIPS on December 7, please check it out.
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 bloem-reddy at stats.ox.ac.uk
Office: Department of Statistics, Room 1.06
Neural network models of exchangeable sequences
B. Bloem-Reddy and Y. W. Teh
Submitted to the NIPS 2018 Workshop on Bayesian Deep Learning
Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks
B. Bloem-Reddy, A. Foster, E. Mathieu, Y. W. Teh
[uai] [arxiv] [code]
Preferential Attachment and Vertex Arrival Times
B. Bloem-Reddy and P. Orbanz
(Sampling and Inference for Beta Neutral-to-the-Left 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"
Journal of the Royal Statistical Society, Series B (Statistical Methodology), 79(5)
[jrss b] [pdf] [slides from discussion at RSS meeting]
Random-walk models of networks formation and sequential Monte Carlo methods for graphs
B. Bloem-Reddy and P. Orbanz
Journal of the Royal Statistical Society, Series B (Statistical Methodology), 80(5), 871-898
[jrss b] [arxiv] [code]
[A talk on this work] [Another talk on this work, by Peter Orbanz]
Slice Sampling on Hamiltonian Trajectories
B. Bloem-Reddy and J. P. Cunningham
ICML 2016: JMLR W+CP
[pdf] [icml/jmlr] [A talk on this work]
Superfluid Phase Stability of 3He 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
Stability of the axial phase of superfluid 3He 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)
Sequential sampling of Gaussian process latent variable models
M. Tegner, B. Bloem-Reddy, S. Roberts
ICML 2018 Workshop on Tractable Probabilistic Models
Sampling and inference for discrete random probability measures in probabilistic programs
B. Bloem-Reddy*, E. Mathieu*, A. Foster, T. Rainforth, Y. W. Teh, M. Lomeli, H. Ge, Z. Ghahramani
NIPS 2017 Workshop on Advances in Approximate Bayesian Inference
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.
Exchangeable random partitions and random discrete probability measures: a brief tour guided by the Dirichlet Process
Notes for a lecture given to Oxford PhD students (these are a work in progress)