SMILING

Statistical MachIne LearnING Reading Group in Bordeaux

Organizers: François Caron, Marie Chavent, Robin Genuer, Adrien Todeschini

Location: INRIA Bordeaux, 200 avenue de la vieille tour, Talence
When: 13h00-14h00 on Tuesday every two weeks

Objectives: Present and discuss recent journal papers/conference proceedings/book chapter from machine learning and statistics on statistical machine learning. Favor discussions and collaborations among researchers interested in this area.

Topics: dimension reduction, unsupervised learning, matrix completion/factorization, latent models, sparse modelling, Bayesian methods, collaborative filtering, clustering, statistical models on graphs, Monte Carlo methods, etc…

If you want to receive information about this reading group, please send an email to
Marie.Chavent@inria.fr or Robin.Genuer@isped.u-bordeaux2.fr

Schedule 2017

Date
Room
Paper
Presenter
May 30, 2017Grace Hooper
(B405, 4th floor)
Variational Learning of Inducing Variables in Sparse Gaussian
Processes , proceedings AISTATS 2009,
https://pdfs.semanticscholar.org/9c13/b87b5efb4bb011acc89d90b15f637fa48593.pdf
Audrey
May 2, 2017Grace Hooper
(B405, 4th floor)
The role of empirical Bayes methodology as a leading principle in modern medical statistics
Hans C. van Houwelingen (2014) ,
https://www.ncbi.nlm.nih.gov/pubmed/25205521
Boris
April 4, 2017Grace Hooper
(B405, 4th floor)
Scalable Simple Random Sampling and Stratified Sampling
Xiangrui Meng (2013)
http://www.jmlr.org/proceedings/papers/v28/meng13a.pdf
Robin
March 21, 2017Grace Hooper
(B405, 4th floor)
Metropolis-Hastings algorithms with autoregressive proposals, and a few examples
Richard A. Norton, Colin Fox (2016). https://arxiv.org/abs/1605.05441
Jean-François
February 14, 2017Grace Hopper
(B405, 4th floor)
Austerity in MCMC Land: Cutting the Metropolis-Hastings Budget. Chen, Yutian, et al..
http://www.ics.uci.edu/~welling/publications/papers/austerity_ICML14_complete.pdf
Pierre
January 17, 2017
Grace Hopper
(B405, 4th floor)
Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares.
Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh (2014).
https://arxiv.org/pdf/1410.2596.pdf
Hadrien
January 3, 2017
Grace Hopper
(B405, 4th floor)
Apprentissage sur Données Massives; trois cas d'usage avec R, Python et Spark.
Philippe Besse, Brendan Guillouet, Jean-Michel Loubes (2016).
https://hal.archives-ouvertes.fr/hal-01350099
Marie

Schedule 2015/2016

Date
Room
Paper
Presenter
June 7, 2016Alan Turing 2

"Mondrian Forests: Efficient Online Random Forests",
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh, 2014
Robin
May 10, 2016Alan Turing 2

"Optimizing random scan Gibbs samplers" de R. A. Levine et G. Casella.Audrey
March 29, 2016Alan Turing 2

Mastering the game of Go with deep neural networks and tree search. David Silver et.al, Nature 2016Pierre
March 15, 2016Grace Hopper 2

Hierarchical Dirichlet Processes, Yee Whye Teh, Michael I. Jordan, Matthew J. Beal and David M. Blei (2006)Chariff
March 1, 2016Grace Hopper 2

The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies.
Blei, D. M., Griffiths, T. L., & Jordan, M. I. (2010). Journal of the ACM (JACM), 57(2), 7.
Jessica
Januray 26, 2016Grace Hopper
(B405, 4th floor)
Overlapping stochastic block models with application to the french political blogosphere. Latouche, P., Birmelé, E., & Ambroise, C. (2011). The Annals of Applied Statistics, 309-336.Adrien
Januray 12, 2016Grace Hopper
(B405, 4th floor)
Regularized Generalized Canonical Correlation Analysis. Arthur Tenenhaus, Michel Tenenhaus. Psychometrika (2011)Hadrien
December 8, 2015Grace Hopper
(B405, 4th floor)
Generalized Additive Model Selection, Alexandra Chouldechova and Trevor HastieMarta
November 24, 2015Grace Hopper
(B405, 4th floor)
Accelerated Gibbs sampling of normal distributions using matrix splittings and polynomials, Colin Fox and Albert ParkerAndrei
November 3, 2015
Grace Hopper
(B405, 4th floor)
MCMC using Hamiltonian dynamics, Radford M. Neal. http://arxiv.org/pdf/1206.1901.pdfJean-François

Schedule 2014/2015

Date
Room
Paper
Presenter
November 18, 2014
Grace Hopper
(B405, 4th floor)
M. Journée, Y. Nesterov, P. Richtarik,, R. Sepulchre. Generalized Power Method for Sparse Principal Component Analysis, Journal of Machine Learning Research, 2010. pdfMarie
December 2, 2014
Alan Turing
(B305, 3d floor)
Nyamundanda, G., Gormley, I. C., Brennan, L. A dynamic probabilistic principal components model for the analysis of longitudinal metabolomics data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 2014. pdfBoris
December 16, 2014
Grace Hopper
(B405, 4th floor)
Confidence intervals for low dimensional parameters in high dimensional linear models. J. R. Statist. Soc. B, 2014, pdfMarta
January 13, 2015
Grace Hopper 2
(4th floor)
Madigan, J. A., Hoeting, D., Raftery, C. T., & Volinsky, C. T., Bayesian model averaging: A tutorial. Statistical Science, 44(4), 1999, pdfPierre
January 27, 2015
Grace Hopper 2
(4th floor)
Arribas-Gil A., Bertin C., Meza C., Rivoirard V., LASSO-type estimators for semiparametric nonlinear mixed-effects models estimation, Statistics and Computing, 24:3, 2014 pdfPerrine
February 10, 2015
A304
(3d floor)
Xiong, Liang, et al. "Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization." SDM. Vol. 10. 2010. pdfAdrien
Mach 10, 2015
Grace Hooper
(4th floor)
S. Chib et I. Jeliazkov, Marginal Likelihood From the Metropolis–Hastings Output, JASA 2001. pdfAndrei
Mach 24, 2015
Grace Hooper
(4th floor)
Second-Order Latent-Space Variational Bayes for Approximate Bayesian Inference, IEEE signal processing letters Vol. 15, 2008. pdfJessica
June 16, 2015
Grace Hooper
(4th floor)
On statistics, computation and scalability, M. Jordan (2013), The Big Data Bootstrap, Kleiner, Talwalkar, Sarkar, Jordan (2012)Robin

Schedule 2013/2014

Date
Room
Paper
Presenter
January 22, 2013Grace Hopper
(B404, 4th floor)
R. Mazumber, T. Hastie, R. Tibshirani. Spectral Regularization Algorithms for Learning Large Incomplete Matrices. Journal of Machine Learning Research, vol. 11, pp. 2287-2322, 2010. pdf
See also the talk at the conference compstat. pdf
François
February 5, 2013Grace Hopper
(B404, 4th floor)
T. Hesterberg, N.H. Choi, L. Meier, C. Fraley. Least angle and l1 penalized regression: a review. Statistics Survey, 2008. pdfMarie
February 19, 2013Grace Hopper
(B404, 4th floor)
C.J.C. Burges. Dimension reduction: a guided tour. Foundations and trends in machine learning, 2010. pdf
Chapters 1-3
Pierre
March 5, 2013Grace Hopper
(B404, 4th floor)
H. Lee, R. Grosse, R. Ranganath, A. Ng. Convolutional Deep Belief Networks for scalable unsupervised learning of hierarchical representations. International Conference on Machine Learning, 2009. pdfOlivier
April 2, 2013Grace Hopper
(B404, 4th floor)
U. Von Luxburg. A tutorial on spectral clustering. Statistics and Computing, 2007. pdfRobin
May 21, 2013Grace Hopper
(B404, 4th floor)
J. Moller, AN Pettitt, R. Reeves, KK Berthelsen. An efficient Markov chain Monte Carlo method for distributions with intractable normalising constants. Biometrika, 2006. pdf
I. Murray, Z. Ghahramani, D. MacKay. MCMC for doubly-intractable distributions. UAI, 2006. pdf
Jean-François
June 4, 2013Grace Hopper
(B404, 4th floor)
M. Wainwright, M.I. Jordan. Graphical Models, Exponential families and variational inference. Foundations and trends in machine learning, 2008. Chapter 3. pdfFrançois
June 18, 2013Grace Hopper
(B404, 4th floor)
M. Wainwright, M.I. Jordan. Graphical Models, Exponential families and variational inference. Foundations and trends in machine learning, 2008. Chapters 5-6. pdfFrançois
October 15, 2013Grace Hopper
(B404, 4th floor)
A., Bellavance, F., & Larocque, D. (2012). Mixed-effects random forest for clustered data. Journal of Statistical Computation and Simulation, 1-16. pdfBoris
November 5, 2013Alan Turing
(B304, third floor)
T. Griffiths & Z. Ghahramani. The Indian Buffet process: an introduction and review. Journal of Machine Learning Research, 2011. pdfAdrien
December 3, 2013Grace Hooper 1
(B404, 4th floor)
T. Griffiths & Z. Ghahramani. The Indian Buffet process: an introduction and review. Journal of Machine Learning Research, 2011. pdfAdrien
December 17, 2013Grace Hooper 1
(B404, 4th floor)
S. Roberts & G.Nowak. Stabilizing the lasso against cross-validation variability. Comput. Stat. Data Anal. , 2013. pdfFrédéric
Mars 4, 2014Grace Hooper
(B404, 4th floor)
O. Cappé. Online EM Algorithm for Hidden Markov Models, J. Comput. Graph. Statistics, 2011. pdfPierre
May 20, 2014Grace Hooper 1
(4th floor)
Cappé, O. and Moulines, E., On-line expectation-maximization algorithm for latent data models, J. Roy. Statist. Soc. B, 2009. pdfAudrey
May 27, 2014Grace Hooper 1
(4th floor)
D.P. Helmbold, P.M. Long. On the Necessity of Irrelevant Variables. Journal of Machine Learning Research 2012. pdfRobin
Juin 24, 2014Allan Turing 1, (3rd floor)Blei, D. M., Ng, A. Y., & Jordan, M. I. Latent Dirichlet Allocation. The Journal of machine Learning research. 2003.
pdf
Olivier