Benjamin BloemReddy
I am Assistant Professor of Statistics at the University of British Columbia. I work on problems in statistics and machine learning, with an emphasis on probabilistic approaches. My recent research has focused on developing methods for evolving networks whose history is unobserved; on distributional limits of preferential attachment networks; and on uses of symmetry in statistics, computation, and machine learning. My work has used and developed methods in Bayesian nonparametrics, sequential Monte Carlo, and probabilistic symmetries.
I was a PhD student with Peter Orbanz at Columbia and a postdoc with Yee Whye Teh in the CSML group at the University of Oxford. Before moving to statistics and machine learning, I studied physics at Stanford University and Northwestern University.
Contact: benbr at stat dot ubc dot ca
Office: Department of Statistics, Earth Sciences Building, Room 3168
Teaching

Fall 2020: STAT 547C, Topics in Probability for Statistics
[course website]

STAT 548 qualifying papers
[guide, expectations, and list of potential papers]
Past courses

Fall 2019: STAT 547C, Topics in Probability
[course website]
Notes

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]
Papers

On the Benefits of Invariance in Neural Networks
C. Lyle, M. van der Wilk, M. Kwiatkowska, Y. Gal, B. BloemReddy
[arxiv]

Probabilistic symmetry and invariant neural networks
B. BloemReddy and Y. W. Teh
Journal of Machine Learning Research
[jmlr] [arxiv] [slides from a talk on this work] [Yee Whye talked about this in his IMS Medallion Lecture] 
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] 
Invariance and Generalization Bounds
C. Lyle and B. BloemReddy
NeurIPS 2019 Workshop on Machine Learning with Guarantees 
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.