Papers etc
Lecture notes
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• Bayesian Nonparametrics
[PDF]
• Machine Learning (Slides)
[PDF]
• Probability Theory II
[PDF]
Papers
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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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Designing Mechanical Meta-Materials by Learning Equivariant Flows.
M Mirramezani, AS Meeussen, K Bertoldi, P Orbanz, and RP Adams.
[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]
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The Graphical Matrix Pencil Method: Exchangeable Distributions with Prescribed Subgraph Densities.
LM Gunderson, G Bravo-Hermsdorff, and P Orbanz.
NeurIPS 2023.
[Paper]
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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]
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Uniform estimation in stochastic block models is slow.
With I Castillo.
Electronic Journal of Statistics, Vol. 16, No. 1, 2947-3000, 2022.
[arxiv]
[Journal]
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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]
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Distribution theory for hierarchical processes.
With
F Camerlenghi,
A Lijoi
and
I Prünster.
Annals of Statistics, Vol. 47, 67-92, 2019.
[Journal]
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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]
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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]
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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]
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Subsampling large graphs and invariance in networks.
P Orbanz.
[arxiv]
[A talk on this work]
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Preferential attachment and vertex arrival times.
With
B Bloem-Reddy.
[arxiv]
[An article on inference in these models]
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Independence by random scaling.
With
LF James.
[arxiv]
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Borel liftings of graph limits.
With B Szegedy.
Electronic Communications in Probability, Vol. 21, paper no. 65, 2016.
[PDF]
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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]
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Scaled subordinators and generalizations of the Indian buffet process.
With
LF James
and
YW Teh.
[arxiv]
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Nonparametric priors on complete separable metric spaces.
P Orbanz.
[PDF]
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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)]
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Projective Limit Random Probabilities on Polish Spaces.
P Orbanz.
Electronic Journal of Statistics, Vol. 5, 1354-1373, 2011.
[PDF]
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Unit-rate Poisson representations of completely random measures.
With S Williamson.
[PDF]
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Dependent Indian Buffet Processes.
S Williamson,
P Orbanz
and
Z Ghahramani.
AISTATS 2010, JMRL W&CP 9:924-931.
[PDF]
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Conjugate Projective Limits.
P Orbanz.
[arxiv]
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Bayesian Nonparametric Models.
P Orbanz
and
YW Teh.
In Encyclopedia of Machine Learning. Springer, 2010.
[PDF]
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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)
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Functional Conjugacy in Parametric Bayesian Models.
P Orbanz.
Techreport, 2009.
[PDF]
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Music Preference Learning with Partial Information.
Y Moh,
P Orbanz
and
JM Buhmann.
ICASSP 2008.
[PDF]
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Nonparametric Bayesian Image Segmentation.
P Orbanz and JM Buhmann.
International Journal of Computer Vision (IJCV), Vol. 77, 25-45, 2008.
[PDF]
 
[Journal]
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Cluster Analysis of Heterogeneuos Rank Data.
LM Busse, P Orbanz and JM Buhmann.
International Conference on Machine Learning (ICML), 2007.
[PDF]
 
[PDF (with corrections)]
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Bayesian Order-Adaptive Clustering for Video Segmentation.
P Orbanz, S Braendle and JM Buhmann.
EMMCVPR, 2007.
[PDF]
 
[Publisher]
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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]
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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]