Bayesian Nonparametrics

In the BNP reading group we discuss Statistics and Machine learning papers related to Bayesian nonparametrics.

• When? Every Tuesday 15:00pm-16:00pm
• Where? Microsoft Teams, link available upon request

Schedule TT 2021

Organizer: Caroline Lawless and Deborah Sulem

Date	Presenter	Title	Link
20/04/2021	Bo Ning	Topological Data Analysis
27/04/2021	Deborah	Bayesian Manifold Regression	https://arxiv.org/pdf/1305.0617.pdf
04/05/2021	No meeting (MFO workshop)
11/05/2021	Francesca	How good is the Bayes posterior in DNN ?	https://arxiv.org/abs/2002.02405

Schedule HT 2021

Organizer: Caroline Lawless and Deborah Sulem

Date	Presenter	Title	Link
12/01/2021	Dan, Badr, Cian	NeurIPS20 Session 1	https://arxiv.org/pdf/2005.12601.pdf, https://arxiv.org/pdf/1912.08335.pdf, https://papers.nips.cc/paper/2020/hash/db957c626a8cd7a27231adfbf51e20eb-Abstract.html
19/01/2021	Caroline, Pierre, Deborah	NeurIPS20 Session 2	https://arxiv.org/abs/2010.11665, https://proceedings.neurips.cc/paper/2020/file/32fcc8cfe1fa4c77b5c58dafd36d1a98-Paper.pdf, https://proceedings.neurips.cc/paper/2020/file/1fd09c5f59a8ff35d499c0ee25a1d47e-Paper.pdf
27/01/2021	Judith	Synthetic Data Generators - Sequential and Private	https://proceedings.neurips.cc/paper/2020/file/4eff0720836a198b6174eecf02cbfdbf-Paper.pdf
02/02/2021	Francesca	Understanding Double Descent	https://arxiv.org/abs/2011.03321,
09/02/2021	Jun	Convergence complexity of MCMC in high-dimensional settings
16/02/2021	Cian	Prediction of Information Cascades	https://arxiv.org/abs/2009.02092
23/02/2021	Deborah	Berstein von Mises Theorems for functionals of covariance matrices	https://projecteuclid.org/euclid.ejs/1468849963
02/03/2021	Judith	High dimensional Bayesian clustering	https://arxiv.org/pdf/2006.02700.pdf
09/03/2021	Caroline	Hierarchical Dirichlet Processes	https://www.tandfonline.com/doi/pdf/10.1198/016214506000000302?casa_token=UNYsCGrnNaMAAAAA:dews-aD4HOwqGcJuJn_wi0Mi4-81Nr_9xIbiEIY73E2GXBZMSZaEJGEChJfAeOMfTuCWBZTzUEYIgw
16/03/2021	Daniel	Density estimation on an unknown submanifold	https://arxiv.org/abs/1910.08477
23/03/2021	Pierre	Stochastic Gradient Descent as Approximate Bayesian Inference	https://www.jmlr.org/papers/volume18/17-214/17-214.pdf
30/03/2021	Badr	Stochastic Gradient Descent algorithms for Variational Inference	https://arxiv.org/abs/1401.0118, https://arxiv.org/pdf/1806.04854.pdf

Schedule MT 2020

Organizer: Alisa Kirichenko

Date	Presenter	Title	Link
06/10/2020	Matteo Giordano	Consistent Bayesian inference in elliptic nonlinear inverse problems
13/10/2020	Badr	A theoretical study of variational inference
20/10/2020	Gregoir Clarte	Statistical framework for the reconstruction of the history of sign languages
27/10/2020	Caroline	Approximate Bayesian computation with the Wasserstein distance	[1]
03/11/2020	CANCELLED	Everyone is encouraged to attend Bayes Club instead
10/11/2020	Dan	Bayesian inference for spline-based hidden Markov models	[1]
17/11/2020	CANCELLED	Everyone is encouraged to attend BAYSM instead	https://j-isba.github.io/baysmo.html
24/11/2020	Pierre Wolinski	Variational Inference for Neural Networks and Bayesian Interpretation of Penalties
01/12/2020	CANCELLED	Everyone is encouraged to attend Bayes Club instead
08/12/2020	Rianne de Heide	Bayesian Best-Arm Identification	[1]
15/12/2020	Francesca	Most important statistical ideas of the past 50 years	https://arxiv.org/abs/2012.00174

Schedule HT and TT 2020

Organizer: Alisa Kirichenko

Date	Presenter	Title	Link
29/01/2020	Everyone	NeurIPS meeting	List of articles
05/02/2020	Deborah, Francesca, James	NeurIPS meeting II	List of articles
12/02/2020	CANCELLED
19/02/2020	Deborah	Consistency results under weak assumptions	New approaches to Bayesian consistency A novel approach to Bayesian consistency
26/02/2020	Giuseppe	Differential privacy
04/03/2020	Caroline	[1] Approximate Bayesian Computation: a nonparametric perspective	[1]
01/04/2020	Cian	[1] Automated Scalable Bayesian Inference via Hilbert Coresets [2] Sparse Variational Inference: Bayesian Coresets from Scratch	[1], [2]
08/04/2020	Francois	Stopping explosion by penalising transmission to hubs in scale-free spatial random graphs	https://arxiv.org/abs/1805.05054
15/04/2020	Judith	Convergence rates for variational Bayes	https://arxiv.org/pdf/1712.02519
22/04/2020	Dan	Applications of the van Trees inequality	https://projecteuclid.org/euclid.bj/1186078362
29/04/2020	CANCELLED
06/05/2020	James	Generalized Variational Inference and Consistency
13/05/2020	Alice	Fast and scalable non-parametric Bayesian inference for Poisson point processes	https://arxiv.org/abs/1804.03616
20/05/2020	Francesca	Bayesian inference for brain connectomes
27/05/2020	Cian	Bootstrapping Exchangeable Random Graphs	https://arxiv.org/abs/1711.00813
03/06/2020	Deborah	Approximate leave-future-out cross-validation for Bayesian time series models	https://arxiv.org/abs/1902.06281
10/06/2020	Samuel	Stein Variational Gradient Descent	[1],[2]
17/06/2020	Fadhel	Wasserstein Learning of Deep Generative Point Process Models	[1]
24/06/2020	Jan van Waaij	Uncertainty quantification for stochastic block models

Some papers/topics/books

• NONPARAMETRIC BAYESIAN MULTI-ARMED BANDITS FOR SINGLE CELL EXPERIMENT DESIGN https://arxiv.org/pdf/1910.05355.pdf
• LIMITS OF SPARSE CONFIGURATION MODELS AND BEYOND: GRAPHEXES AND MULTI-GRAPHEXES https://arxiv.org/pdf/1907.01605.pdf
• Efficient Graph Generation with Graph Recurrent Attention Networks. https://arxiv.org/pdf/1910.00760.pdf
• Emmanuel Abbe and Colin Sandon: Achieving the KS threshold in the general stochastic block model with linearized acyclic belief propagation. https://papers.nips.cc/paper/6365-achieving-the-ks-threshold-in-the-general-stochastic-block-model-with-linearized-acyclic-belief-propagation.pdf
• Aurelien Decelle, Florent Krzakala, Cristopher Moore, Lenka Zdeborová: Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. https://arxiv.org/abs/1109.3041
• Benjamin Bloem-Reddy, Adam Foster, Emile Mathieu, Yee Whye Teh: Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks https://arxiv.org/abs/1807.03113
• Chao Gao and Zongmin Ma. Minimax Rates in Network Analysis: Graphon Estimation, Community Detection and Hypothesis Testing. Nov. 2018. https://arxiv.org/pdf/1811.06055.pdf
• Chao Gao, Jiyi Liu, Yuan Yao, Weizhi Zhu. Robust Estimation and Generative Adversarial Nets. Oct. 2018. https://arxiv.org/pdf/1810.02030.pdf
• Barthelmé, S., and Chopin, N. The Poisson transform for unnormalised statistical models. Statistical Computing, 2015. https://arxiv.org/pdf/1406.2839.pdf
• Jim Griffin and Fabrizio Leisen. Modelling and Computation Using NCoRM Mixtures for Density Regression. Bayesian Analysis 2018. https://projecteuclid.org/download/pdfview_1/euclid.ba/1508983454
• S. Xiao et al. Wasserstein Learning of Deep Generative Point Process Models. NIPS 2017. https://papers.nips.cc/paper/6917-wasserstein-learning-of-deep-generative-point-process-models
• M. Farajtabar et al. Fake News Mitigation via Point Process Based Intervention. ICML 2017. https://arxiv.org/pdf/1703.07823.pdf
• V. Veitch, M. Austern, W. Zhou, D. Blei. Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. June 2018. https://arxiv.org/pdf/1806.10701
• A. Bojchevski et al. NetGAN: Generating Graphs via Random Walks. ICML 2018. https://arxiv.org/pdf/1803.00816.pdf
• K. Bringmann et al. Geometric inhomogeneous random graphs. Theoretical Computer Science. 2018. https://www.sciencedirect.com/science/article/pii/S0304397518305309
• J. Komjathy, Bas Lodewijks. Explosion in weighted Hyperbolic Random Graphs and Geometric Inhomogeneous Random Graphs. 2018. https://arxiv.org/abs/1803.04897
• Remco van der Hofstad, Pim van der Hoorn, Nelly Litvak, Clara Stegehuis. Limit theorems for assortativity and clustering in null models for scale-free networks. June 2018. https://arxiv.org/pdf/1712.08097.pdf
• R. van der Hoftstad. Book on Random Graphs and Complex Networks.
• Last and Penrose. Book on Poisson processes.
• Kallenberg. Book on Random measures.
• Resnick. Book on Extreme Values, Regular Variation and Point Processes.
• Ghosal, Van der Vaart. Fundamentals of Nonparametric Bayesian Inference.
• M. Zhou, S. Favaro, S. G. Walker, "Frequency of frequencies distributions and size dependent exchangeable random partitions," to appear in Journal of the American Statistical Association (Theory and Methods).
• Chen et al. Posterior Contraction Rates of Phylogenetic Indian Buffet Processes. Bayesian Analysis.
• Castillo, Orbanz. Uniform estimation of a class of random graph functionals. Arxiv.
• James et al. Scaled subordinators and generalizations of the Indian buffet process. Arxiv.
• James, Orbanz. Independence by Random Scaling. Arxiv.
• Taiji Suzuki. Fast learning rate of deep learning via a kernel perspective.
• Johannes Schmidt-Hieber. Nonparametric regression using deep neural networks with ReLU activation function
• Borgs et al. Sampling perspectives on sparse exchangeable graphs, https://arxiv.org/abs/1708.03237
• Borgs et al. Sparse exchangeable graphs and their limits via graphon processes, https://arxiv.org/abs/1601.07134
• "Sampling and Estimation for (Sparse) Exchangeable Graphs" by Victor Veitch and Daniel M. Roy https://arxiv.org/abs/1611.00843v1
• Survival Analysis, Neutral to the Right (Doksum 1974?)
• Hierachical Normalized Random Measures by Camerlenghi, Pruenster, Lijoi, Orbanz
• book on regular variation by Resnick
• Determinantal Point Processes and Monte Carlo Integration by Remi
• Gaussian approximation of multivariate Levy processes by Cohen & Rosinsky
• Dependent Random Measures
• Compound random measures and their use in Bayesian non-parametrics
  Jim E. Griffin and Fabrizio Leisen
  http://onlinelibrary.wiley.com/doi/10.1111/rssb.12176/pdf
• Modelling and computation using NCoRM mixtures for density regression
  Jim Griffin, Fabrizio Leisen
  https://arxiv.org/abs/1608.00874
• Dependent Normalized Random Measures
  Changyou Chen, Vinayak Rao, Wray Buntine, Yee Whye Teh
  http://www.jmlr.org/proceedings/papers/v28/chen13i.pdf
• A unifying representation for a class of dependent random measures
  Nicholas J. Foti, Joseph D. Futoma, Daniel N. Rockmore, Sinead Williamson
  http://arxiv.org/abs/1211.4753
• Harry Crane on Ewens' Sampling Formula
• Asmussen & Rosinsky, Cohen & Rosinsky on Gaussian tail approximations of Levy processes
• Tree processes (DDT, PYDT, coalescent, Gibbs fragmentation, continuum random trees?)
• Neil Sheppard on Levy processes
• GP inducing points

Past

Schedule MT 2019. Organizer: Xenia Miscouridou.

Date	Presenter	Link	Title
26/09/2019	Francois	https://arxiv.org/pdf/1712.03889.pdf	Statistical Sparsity
3/10/2019	Xenia	http://proceedings.mlr.press/v97/mehta19a/mehta19a.pdf	Stochastic Blockmodels meet Graph Neural Nets
10/10/2019	Olga	http://proceedings.mlr.press/v97/xu19e.html	Variational Russian Roulette for Deep Bayesian Nonparametrics
15/10/2019	James	http://proceedings.mlr.press/v97/chen19b/chen19b.pdf	Stein Point Markov Chain Monte Carlo
10/12/2019	NeurIPS week

Schedule HT and TT 2019. Organizer: Xenia Miscouridou.

Date	Presenter	Topic	Materials
07/02/2019	Xenia	Miscouridou, Caron, Teh. NeurIPS 2018	http://papers.nips.cc/paper/7502-modelling-sparsity-heterogeneity-reciprocity-and-community-structure-in-temporal-interaction-data
14/02/2019	Chris C	Learning of Weighted Dynamic Multi-layer Networks via Latent Gaussian Processes
21/02/2019	Ben	Bloem-Reddy and Teh	http://www.stats.ox.ac.uk/~bloemred/assets/papers/prob-symm-nn-bbr-teh.pdf
28/02/2019	Giuseppe	ICLR 19	https://openreview.net/pdf?id=BJgRDjR9tQ
07/03/2019	Cedric Archambeau's talk		Learning Representations to Accelerate Hyperparameter Tuning
14/03/2019	Fadhel		https://arxiv.org/abs/1902.04714?fbclid=IwAR3N8M6oPcVSIr7fOBddlNi2bEqIF3sN0_ZJleMDkigLlpvEchXlUMBQEmg
21/03/2019	Francesca		https://arxiv.org/pdf/1803.00816.pdf?fbclid=IwAR2dus1S_-4Qatj6-9q31vWT2F3jVduWTh3oQv0CSmHqHjc18KiFGM0MpHc
28/03/2019	Cian		https://arxiv.org/abs/1608.00874?fbclid=IwAR1bIiD8DTIja9pG7JAcgixFTcjUkImhKrGaDDBsrpv21A3QcW9ruSMSGWs
04/04/2019	NO MEETING
11/04/2019	NO MEETING
18/04/2019	NO MEETING
20/06/2019	Jung, Lee, Staton, Yang 2018		https://arxiv.org/abs/1812.06282

Schedule MT 2018. Organizer: Juho Lee.

Date	Presenter	Topic	Materials
21/09/2018	François	Krzakal et al. 2013	http://www.pnas.org/content/pnas/early/2013/11/22/1312486110.full.pdf
12/10/2018	Giuseppe	Corff et al. 2018	https://arxiv.org/abs/1808.08104
18/10/2018	Juho	Rosinski 2001	http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.606.8241&rep=rep1&type=pdf
01/11/2018	Francesca	Huggins et al. 2018	https://arxiv.org/abs/1806.10234
08/11/2018	Sonia	Caldarelli et al. 2013, Aletti et al. 2017	https://journals.aps.org/pre/abstract/10.1103/PhysRevE.87.020106, https://projecteuclid.org/euclid.aoap/1513328714
15/11/2018	François	Voitalov et al. 2018	https://arxiv.org/abs/1811.02071
22/11/2018	Judith	Gao and Lafferty 2017	https://arxiv.org/abs/1710.00862
29/11/2018	Cian	Farajtabar et al. 2017	https://arxiv.org/abs/1703.07823
06/12/2018	Fadhel	Nonnegative Bayesian nonparametric factor models
13/12/2018	Everyone	NeurIPS 2018	https://papers.nips.cc/book/advances-in-neural-information-processing-systems-31-2018
31/12/2018	Juho	Arrival time augmentation for CRM

Schedule HT and TT 2018. Organizer: Marco Battiston.

Date	Presenter	Topic	Materials
12/01/2018	Marco	Book: Ghosal, Van der Vaart.	Chapter 4.5-4.7
19/01/2018	Fadhel	Book: Ghosal, Van der Vaart.	Chapter 5
26/01/2018	Giuseppe	Nguyen 2013	http://dept.stat.lsa.umich.edu/~xuanlong/Papers/Nguyen-AOS-12.pdf
02/02/2018	Judith	Szabo, Van Zanten 2017	https://arxiv.org/pdf/1711.03149.pdf
09/02/2018	Clara	Book: Ghosal, Van der Vaart.	Chapter 6.1
16/02/2018	Xenia	Orbanz 2017	https://arxiv.org/pdf/1710.04217.pdf
23/02/2018	Clara	Book: Ghosal, Van der Vaart.	Chapter 6.2
02/03/2018	Juho	Lee, James, Choi 2016. Lee et al. 2017	https://arxiv.org/pdf/1702.08239.pdf http://mlg.postech.ac.kr/~stonecold/papers/nips2016.pdf
09/03/2018	Marco	Durante, Dunson, Vogelstein 2017	https://amstat.tandfonline.com/doi/pdf/10.1080/01621459.2016.1219260?needAccess=true
13/04/2018	Francois	scale-free networks and degree distributions
27/04/2018	Fadhel	Zhang, Gao 2018	https://arxiv.org/pdf/1712.02519.pdf
11/05/2018	Judith	Jordan, Lee, Yang 2018	https://arxiv.org/abs/1605.07689
25/05/2018	Juho	Bollobas, Janson, Riordan 2006	https://arxiv.org/abs/math/0504589
01/06/2018	Fadhel	on the missing mass estimation
22/06/2018	Xenia	Chen et al. 2013. Foti et al. 2012	http://proceedings.mlr.press/v28/chen13i.pdf https://arxiv.org/pdf/1211.4753.pdf
06/07/2018	Everyone	ICML 2018	https://icml.cc/Conferences/2018/Schedule?type=Poster
27/07/2018	Giuseppe	Knapik, Salomond 2014	https://arxiv.org/abs/1407.0335

Schedule MT 2017. Organizer: Marco Battiston.

Date	Presenter	Topic	Materials
15/09/2017	Marco	Petrone, Rousseau, Scricciolo	https://academic.oup.com/biomet/article/101/2/285/1776208/Bayes-and-empirical-Bayes-do-they-merge
22/09/2017	Fadhel	Book: Ghosal, Van der Vaart.	Chapter 3, sections 3.1-3.4
29/09/2017	Francois	Barndorff-Nielsen, Lunde, Shepard, Veraart	http://onlinelibrary.wiley.com/doi/10.1111/sjos.12056/epdf
13/10/2017	Giuseppe	Book: Ghosal, Van der Vaart.	Chapter 3, sections 3.5-3.7
20/10/2017	Xenia	Yurochkin, Nguyen	https://arxiv.org/pdf/1610.09034.pdf
27/10/2017	Marco	Book: Ghosal, Van der Vaart.	Chapter 4, Sections 4.1-4.2
03/11/2017	Konstantina	Xu, Xu, Saria	https://arxiv.org/pdf/1608.05182.pdf
17/11/2017	Ben	Bloem-Reddy, Orbanz	https://arxiv.org/pdf/1710.02159.pdf
24/11/2017	Marco	Book: Ghosal, Van der Vaart.	Chapter 4, Sections 4.3.1-4.3.3
01/12/2017	Chris	Carmona, Nieto-Barajas, Canale	https://arxiv.org/pdf/1612.00083v2.pdf
08/12/2017	Francois	Book: Ghosal, Van der Vaart.	Chapter 4.3.4-4.4
15/12/2017	everybody	review of some NIPS papers

Earlier meetings.

Date	Presenter	Topic	Materials
14/10/14	Maria	T. Broderick, at al 2013. Cluster and Feature Modeling from Combinatorial Stochastic Processes. Statistical Sciences, 2013
21/10/14	(Visitor) Marc Diesenroth
28/10/14	Yee Whye	Broderick et al. Sections 3-6
04/11/14	(Visitor) Yutian Chen
11/11/14	François	J. Griffin and F. Leisen. Compound random measures and their use in Bayesian nonparametrics. Arxiv preprint, 2014	http://arxiv.org/abs/1410.0611
18/11/14	François and Maria	Griffin and Leisen.
25/11/14	Thibaut	Beck and Teboulle. Mirror descent and nonlinear projected subgradient methods for convex optimization.	http://www.cs.berkeley.edu/~jduchi/papers/BeckTe03.pdf Sections 1-3
02/11/14	Fabian	Beck and Teboulle Sections 4-6
20/01/2015	Balaji	Stochastic Backpropagation and Approximate Inference in Deep Generative Models. Rezende et al	http://machinelearning.wustl.edu/mlpapers/paper_files/icml2014c2_rezende14.pdf
27/01/2015	Tamara	Y. Yu and X.L. Meng. To center of not to center: that is not the question-an ancillarity-sufficiency interweaving strategy for boosting MCMC efficiency. JCGS, 2011	http://dx.doi.org/10.1198/jcgs.2011.203main
03/02/2015	Maria	Y. Yu and X.L. Meng. To center of not to center: that is not the question-an ancillarity-sufficiency interweaving strategy for boosting MCMC efficiency. JCGS, 2011	http://dx.doi.org/10.1198/jcgs.2011.203main
10/02/2015	Frauke, Tamara	P. Muller, F. Quintana and G. Rosner. A product partition model with regression on covariates	http://amstat.tandfonline.com/doi/pdf/10.1198/jcgs.2011.09066
17/02/2015	Thibaut, Balaji	A. Gelman, A. Vehtari, C. Robert, N. Chopin, J.P. Cunningham. Expectation propagation as a way of life	http://arxiv.org/pdf/1412.4869v1
24/02/2015	Thibaut, Balaji	A. Gelman, A. Vehtari, C. Robert, N. Chopin, J.P. Cunningham. Expectation propagation as a way of life	http://arxiv.org/pdf/1412.4869v1
14/04/2015	Maria, Thibaut	Green, Latuszynski, Pereyra, Robert, 2015 � Bayesian computation: a perspective on the current state, and sampling backwards and forwards	http://arxiv.org/pdf/1502.01148v2
28/04/2015	Maria, Thibaut	Green, Latuszynski, Pereyra, Robert, 2015 � Bayesian computation: a perspective on the current state, and sampling backwards and forwards	http://arxiv.org/pdf/1502.01148v2
05/05/2015	Levi, Konstantina	Introduction to Bayesian nonparametrics	http://www.stats.ox.ac.uk/~teh/outbox/jordan-teh.pdf
12/05/2015	Frauke, Tamara	Introduction to Bayesian nonparametrics	http://www.stats.ox.ac.uk/~teh/outbox/jordan-teh.pdf
26/05/2015	Yee_Whye, Maria	Introduction to Bayesian nonparametrics	http://www.stats.ox.ac.uk/~teh/outbox/jordan-teh.pdf
02/06/2015	Konstantina, François	Broderick et al. Beta processes, stick-breaking, and power laws. Bayesian Analysis, 2012.	http://ba.stat.cmu.edu/journal/2012/vol07/issue02/broderick.pdf
09/06/2015	Konstantina, François	Gnedin & Pitman. Notes on the occupancy problem with infinitely many boxes: general asymptotics and power laws. Proba. surveys.	http://projecteuclid.org/euclid.ps/1180728778
20/10/2015	Tamara	Research talk: Gaussian Processes on survival analysis
27/10/2015	Valerio	Research talk
03/11/2015	Seth Flaxman	Research talk
10/11/2015	R. Xu	external speaker
17/11/2015	Konstantina Palla	Research talk
24/11/2015	Dario Spano	external speaker
15/09/2016		Organisational Meeting
22/09/2016	François	"Edge exchangeable models for network data" by Harry Crane and Walter Dempsey	Paper
29/09/2016		No Meeting (Stats Away Day)
06/10/2016	Everyone	Bring A Question Session
13/10/2016	Tamara & Giuseppe	Asymptotics & Posterior Consistency	Notes
20/10/2016	Everyone	Bring A Question Session
27/10/2016	Frauke & Marco	Stochastic Geometry	p. 73-77 in "Poisson Processes" by Kingman; p. 471-475 in "An Introduction to the Theory of Point Processes (Vol II)" by Daley and Vere-Jones
03/11/2016	Everyone	Bring A Question Session
10/11/2016	Konstantina & Xenia	"Exchangeable Trait Allocations"	Paper
17/11/2016	Everyone	Bring A Question Session
24/11/2016	Marco	"The Class of Random Graphs Arising from Exchangeable Random Measures" by Victor Veitch and Daniel M. Roy	Paper
01/12/2016	Everyone	Bring A Question Session
08/12/2016		NIPS
19/01/2017	Everyone	NIPS Review	Papers
26/01/2017	Giuseppe	ch. 8 (Tests and metric entropy) and 9 (Rate of contraction)	Lecture Notes
02/02/2017	Everyone	Bring A Question Session
09/02/2017	Xenia	Finite-Dimensional BFRY Priors and Variational Bayesian Inference for Power Law Models	Paper
16/02/2017	Everyone	Open Session
23/02/2017	Frauke	Poisson-Gamma dynamical systems	Paper
02/03/2017	Everyone	Open Session
09/03/2017	Everyone	On the Consistency of Bayes Estimates	Paper
16/03/2017	Fadhel	Bayesian Poisson Calculus for Latent Feature Modeling via Generalized Indian Buffet Process Priors	Paper
23/03/2017	François	Bayesian inference on random simple graphs with power law degree distributions; Generating simple random graphs with prescribed degree distribution	Lee et al.; Britton et al.