Lecture notes
• Bayesian Nonparametrics
[PDF]
• Machine Learning (Slides)
[PDF]
• Probability Theory II
[PDF]
Papers
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Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes.
H Dance, P Orbanz, and A Gretton.
[arxiv] -
Slow rates of approximation of U-statistics and V-statistics by quadratic forms of Gaussians.
KH Huang and P Orbanz. [arxiv]
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Gaussian universality for approximately polynomial functions of high-dimensional data.
KH Huang, M Austern, and P Orbanz. [arxiv]
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Gaussian and non-Gaussian universality of data augmentation.
KH Huang, P Orbanz, and M Austern. [arxiv]
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Global optimality under amenable symmetry constraints.
P Orbanz. [arxiv]
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Poisson approximation for stochastic processes summed over amenable groups.
H Ye, P Orbanz, and M Austern. [arxiv]
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Representing and learning functions invariant under crystallographic groups.
With RP Adams.
[arxiv] -
Efficiently vectorized MCMC on modern accelerators.
H Dance, P Glaser, P Orbanz, and RP Adams.
ICML 2025.
[arxiv] -
Diagonal Symmetrization of Neural Network Solvers
for the Many-Electron Schrödinger Equation.
KH Huang, N Zhan, E Ertekin, P Orbanz, and RP Adams.
ICML 2025.
[arxiv] -
Distinguishing Cause from Effect with Causal Velocity Models.
J Xi, H Dance, P Orbanz, and B Bloem-Reddy.
ICML 2025.
[arxiv] -
Designing Mechanical Meta-Materials by Learning Equivariant Flows.
M Mirramezani, AS Meeussen, K Bertoldi, P Orbanz, and RP Adams.
ICLR 2025.
[arxiv] -
The Graphical Matrix Pencil Method: Exchangeable Distributions
with Prescribed Subgraph Densities.
LM Gunderson, G Bravo-Hermsdorff, and P Orbanz.
NeurIPS 2023.
[Paper] -
Limit theorems for distributions invariant under groups of transformations.
M Austern and P Orbanz.
Annals of Statistics, Vol. 50, No. 4, 1960–1991, 2022.
[PDF] [Journal] -
Uniform estimation in stochastic block models is slow.
With I Castillo.
Electronic Journal of Statistics, Vol. 16, No. 1, 2947-3000, 2022.
[arxiv] [Journal] -
Empirical risk minimization and stochastic gradient descent for relational data.
V Veitch, M Austern, W Zhou, DM Blei and P Orbanz.
AISTATS 2019.
[arxiv] [Code] -
Distribution theory for hierarchical processes.
With F Camerlenghi, A Lijoi and I Prünster.
Annals of Statistics, Vol. 47, 67-92, 2019.
[Journal] -
Non-vacuous Generalization Bounds at the ImageNet Scale:
a PAC-Bayesian Compression Approach.
W Zhou, V Veitch, M Austern, RP Adams and P Orbanz.
ICLR 2019.
[arxiv] -
Random walk models of network formation and sequential
Monte Carlo methods for graphs.
B Bloem-Reddy and P Orbanz.
Journal of the Royal Statistical Society, Series B, 80, 871-898, 2018.
[Journal] [arxiv] [Code] [A talk on this work] -
Discussion of F. Caron and E. B. Fox, "Sparse graphs using exchangeable random measures."
P Orbanz.
[Discussion] Journal of the Royal Statistical Society, Series B, 79, Part 5.
[PDF] -
Subsampling large graphs and invariance in networks.
P Orbanz.
[arxiv] [A talk on this work] -
Preferential attachment and vertex arrival times.
With B Bloem-Reddy.
[arxiv] [An article on inference in these models] -
Independence by random scaling.
With LF James.
[arxiv] -
Borel liftings of graph limits.
With B Szegedy.
Electronic Communications in Probability, Vol. 21, paper no. 65, 2016.
[PDF] -
Bayesian models of graphs, arrays and other exchangeable random structures.
With DM Roy.
IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, 437-461, 2015.
[Journal] [arxiv]
Some of the key ideas described in this paper are also explained in the following talk:
[Video] -
Scaled subordinators and generalizations of the Indian buffet process.
With LF James and YW Teh.
[arxiv] -
Nonparametric priors on complete separable metric spaces.
P Orbanz.
[PDF] -
Random function priors for exchangeable arrays with applications to graphs and relational data.
JR Lloyd, P Orbanz, Z Ghahramani and DM Roy.
Advances in Neural Information Processing Systems, 2012.
[PDF]   [Talk on this and related work (Video, 50min)] -
Projective Limit Random Probabilities on Polish Spaces.
P Orbanz.
Electronic Journal of Statistics, Vol. 5, 1354-1373, 2011.
[PDF] -
Unit-rate Poisson representations of completely random measures.
With S Williamson.
[PDF] -
Dependent Indian Buffet Processes.
S Williamson, P Orbanz and Z Ghahramani.
AISTATS 2010, JMRL W&CP 9:924-931.
[PDF] -
Conjugate Projective Limits.
P Orbanz.
[arxiv] -
Bayesian Nonparametric Models.
P Orbanz and YW Teh.
In Encyclopedia of Machine Learning. Springer, 2010.
[PDF] -
Construction of Nonparametric Bayesian Models from Parametric Bayes Equations.
P Orbanz.
Advances in Neural Information Processing Systems, 2009.
[PDF]   [Supplements (Proofs)]
[Techreport Version] (Identical text; proofs included as appendix) -
Functional Conjugacy in Parametric Bayesian Models.
P Orbanz.
Techreport, 2009.
[PDF] -
Music Preference Learning with Partial Information.
Y Moh, P Orbanz and JM Buhmann.
ICASSP 2008.
[PDF] -
Nonparametric Bayesian Image Segmentation.
P Orbanz and JM Buhmann.
International Journal of Computer Vision (IJCV), Vol. 77, 25-45, 2008.
[PDF]   [Journal] -
Cluster Analysis of Heterogeneuos Rank Data.
LM Busse, P Orbanz and JM Buhmann.
International Conference on Machine Learning (ICML), 2007.
[PDF]   [PDF (with corrections)] -
Bayesian Order-Adaptive Clustering for Video Segmentation.
P Orbanz, S Braendle and JM Buhmann.
EMMCVPR, 2007.
[PDF]   [Publisher] -
Smooth Image Segmentation by Nonparametric Bayesian Inference.
P Orbanz and JM Buhmann.
European Conference on Computer Vision (ECCV), Vol. 1, 444-457, 2006.
[PDF]   [Publisher] -
SAR Images as Mixtures of Gaussian Mixtures.
P Orbanz and JM Buhmann.
IEEE International Conference on Image Processing (ICIP), Vol. 2, 209-212, 2005.
[PDF]   [Publisher]