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. Some recent examples:
 Uses and benefits of symmetry in statistics, computation, and machine learning.
 Models and inference methods (SMC, MCMC) for evolving processes (e.g., networks, forest fires) whose history is partially or full unobserved.
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
Research group
Current (alphabetical order)
 Boyon Beronov, PhD student (Computer Science)
 Kenny Chiu, MSc (2021) → PhD student
 Gian Carlo Diluvi (with Trevor Campbell), MSc (2021) → PhD student
 Kevin Lam (with Yongjin Park), PhD student
 William Laplante (with Daniel J. McDonald), MSc student
 Quanhan (Johnny) Xi, MSc student (2022) → PhD student
Past (reverse chronological order)
 Quanhan (Johnny) Xi, MSc student (2022) → PhD student, UBC Statistics
 Weijia (Grace) Yin, MSc (2022)
 Kenny Chiu, MSc (2021) → PhD student, UBC Statistics
 Gian Carlo Diluvi (with Trevor Campbell), MSc (2021) → PhD student, UBC Statistics
 Sean La (with Alexandre BouchardCôté), MSc (2021) → Data Scientist, Kabam Games
Papers
Published and preprints

Indeterminacy in Latent Variable Models: Characterization and Strong Identifiability
Q. Xi and B. BloemReddy
[arxiv]

Lossy Compression for Lossless Prediction
Y. Dubois, B. BloemReddy, K. Ullrich, C. J. Maddison
NeurIPS 2021 (Spotlight)
[neurips] [arxiv] [code] [Neural Compression Workshop @ ICLR 2021 version (oral presentation)]

Uncertainty in Neural Processes
S. Naderiparizi, K. Chiu, B. BloemReddy, F. Wood
[arxiv]

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] 
Beauty in Machine Learning: Fluency and Leaps
I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021 (selected for contributed talk)
[paper] [poster] 
Multiple Environments Can Reduce Indeterminacy in VAEs
Q. Xi and B. BloemReddy
NeurIPS Workshop on Causal Inference & Machine Learning: Why now? (WHY21)
[paper] [poster] 
Generalization Bounds for Invariant Neural Networks
C. Lyle, B. BloemReddy, Y. Gal, M. Kwiatkowska
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.
In progress (comments welcome)
Workshop contributions
(Most of these have a more fully developed counterpart above)Teaching
Current

Fall 2022: STAT 547C, Topics in Probability for Statistics
[course website]

STAT 548 qualifying papers
[guide, expectations, and list of potential papers]
Upcoming
Spring 2022: STAT 305, STAT 547S: Topics on Symmetry in Statistics and MLPast courses
 Summer 2020, Spring 2020, Spring 2021: STAT 305, Introduction to Statistical Inference
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]