PIGSTwoThousandAndEleven < ANC

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---+ Probabilistic Inference Group (!PIGS) - Archive

---++ 
---++ Meetings in 2011
---+++ *29 November: Peter Orchard*
   * [[http://arxiv.org/pdf/1111.0416][Modeling Item-Item Similarities for Personalized Recommendations on Yahoo! Front Page]]: Agarwal et. al,, Annals of applied Statistics (2011)
   * [[http://arxiv.org/ps/1110.5508v1][A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso]]: Mazumder and Agarwal: preprint (2011)
---+++ *15 November: Guido Sanguinetti*
   * [[http://rsif.royalsocietypublishing.org/content/6/31/187.full.pdf+html][Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems]]: Toni et al., JRSI (2009)
   * [[http://www.pnas.org/content/108/37/15190.full?sid=77b621ac-85c8-4ba9-8eec-5d5c064011d8][Bayesian design of synthetic biological systems]]: Barnes, et al., PNAS (2010)
---+++ *1 November:* Ioan Stanculescu

We will discuss the following papers:

   * Lei Li, B. Aditya Prakash: [[http://www.icml-2011.org/papers/159_icmlpaper.pdf][Time Series Clustering: Complex is Simpler!]]. ICML 2011.
   * Manuel Gomez Rodriguez, David Balduzzi, Bernhard Schölkopf: [[http://www.icml-2011.org/papers/354_icmlpaper.pdf][Uncovering the Temporal Dynamics of Diffusion Networks]]. ICML 2011.
---+++ *18 October:* Ali Eslami

We will discuss the following papers:

   * K-H Cho, T. Raiko, A. Hin: [[http://www.icml-2011.org/papers/98_icmlpaper.pdf][Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines]]. ICML 2011.
   * B. Schwehn: [[http://www.inf.ed.ac.uk/publications/thesis/online/IM100897.pdf][Using the Natural Gradient for training Restricted Boltzmann Machines]]. M.Sc thesis.
---+++ 4th October: UAI review

We will review the proceedings of [[http://auai.org/uai2011/accepted.html][UAI 2011]]. If you would like to discuss a paper, please edit this section to include the title together with your initials.

SL: [[http://uai.sis.pitt.edu/papers/11/p410-knowles.pdf][Pitman-Yor Diffusion Trees]] - Knowles, Ghahramani

AJS: [[http://uai.sis.pitt.edu/papers/11/p337-poon.pdf][Sum Product Networks]] - Poon, Domingos

NH: [[http://uai.sis.pitt.edu/papers/11/p283-gutmann.pdf][Bregman divergence as general framework to estimate unnormalized statistical models]] - Gutmann, Hirayama

CKIW: [[http://uai.sis.pitt.edu/papers/11/p514-mnih.pdf][Conditional Restricted Boltzmann Machines for Structured Output Prediction]] -- Mnih, Larochelle, Hinton

DR: [[http://uai.sis.pitt.edu/papers/11/p463-louradour.pdf][Classification of Sets using Restricted Boltzmann Machines]] -- Louradour, Larochelle

---+++ 20th September: Matthias Bethge
   * Guest lecture by Matthias Bethge.

---+++ Tue August 23rd - Shahzad Asif
   * C. Pamminger and S. Fruhwirth-Schnatter, &ldquo;<a href="http://ba.stat.cmu.edu/journal/2010/vol05/issue02/pamminger.pdf" title="Model-based clustering of categorical time series">Model-based clustering of categorical time series</a>,&rdquo; Bayesian Analysis, vol. 5, no. 2, pp. 345&ndash;368, 2010.
---+++ Tue 9th August - Yichuan Zhang

   * A. U. Asuncion, Q. Liu, A.T. Ihler, P. Smyth: [[http://www.ics.uci.edu/~asuncion/pubs/ICML_10.pdf][Particle Filtered MCMC-MLE with Connections to Contrastive Divergence]]. ICML 2010
---+++ Tue 26 July - "ICML 2011"
   * FA: A. Vattani, D. Chakrabarti, M. Gurevich: [[http://www.icml-2011.org/papers/434_icmlpaper.pdf][Preserving Personalized Pagerank in Subgraphs]]
   * CW: R. Socher, C. Lin, A. Y. Ng, and C. D. Manning: [[http://www.icml-2011.org/papers/125_icmlpaper.pdf][Parsing Natural Scenes and Natural Language with Recursive Neural Networks]]
   * PRO: X. Zhang, D. Dunson, L. Carin: [[http://www.icml-2011.org/papers/432_icmlpaper.pdf][Tree-Structured Infinite Sparse Factor Model]]
   * IM: Max Welling and Yee Whye Teh: [[http://www.icml-2011.org/papers/398_icmlpaper.pdf][Bayesian learning via stochastic gradient Langevin dynamics]]
   * FD: F. Doshi et al.: [[http://www.icml-2011.org/papers/483_icmlpaper.pdf][Infinite Dynamic Bayesian Networks]]
---+++ Tue 12 July - Athina Spiliopoulou

We will discuss the following paper:
   * Andrew Saxe, pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Ng: [[http://www.icml-2011.org/papers/551_icmlpaper.pdf][On Random Weights and Unsupervised Feature Learning]], ICML, 2011

---+++ Tue 28 June - Andrea Ocone

We will discuss the following papers:
   * Andreas Ruttor, Manfred Opper: [[http://prl.aps.org/pdf/PRL/v103/i23/e230601][Efficient statistical inference for stochastic reaction processes]], Phys Rev Lett, 2009
   * Andreas Ruttor, Manfred Opper: [[http://jmlr.csail.mit.edu/proceedings/papers/v9/ruttor10a/ruttor10a.pdf][Approximate parameter inference in a stochastic reaction-diffusion model]], Aistats, 2010
---+++ Tue 14 June - talk by Michalis Titsias

Title: Sparse Variational Inference for Multi-Task Learning
---+++ Tue 17 May - "AISTATS 2011"

Please add your paper nominations together with your initials.

   * JK: A. Courville, J. Bergstra, and Y. Bengio: A Spike and Slab Restricted Boltzmann Machine
   * GS: Chris Bracegirdle, David Barber: Switch-Reset Models : Exact and Approximate Inference
   * AS: Frederik Eaton: [[http://jmlr.csail.mit.edu/proceedings/papers/v15/eaton11a/eaton11a.pdf][A conditional game for comparing approximations]]
   * CW: _Jaakko Peltonen, Samuel Kaski: [[http://jmlr.csail.mit.edu/proceedings/papers/v15/peltonen11a/peltonen11a.pdf][Generative Modeling for Maximizing Precision and Recall in Information Visualization]]_
   * AE: I'd actually like to do the Larochelle and Murray paper if Iain isn't interested in doing it himself: H. Larochelle, I. Murray: [[http://jmlr.csail.mit.edu/proceedings/papers/v15/larochelle11a/larochelle11a.pdf][The Neural Autoregressive Distribution Estimator]]. (That's fine, IM)
   * IM: Two papers on matrix factorization: Lakshminarayanan, Bouchard and Archambeau, [[http://jmlr.csail.mit.edu/proceedings/papers/v15/lakshminarayanan11a/lakshminarayanan11a.pdf][Robust Bayesian Matrix Factorisation]] and Balan, Boyles, Welling, Kim and Park [[http://jmlr.csail.mit.edu/proceedings/papers/v15/balan11a/balan11a.pdf][Statistical Optimization of Non-Negative Matrix Factorization]]
---+++ Tue 3 May - Ronald Begg

We will discuss the following paper:
   * Airoldi, Blei, Fienberg and Xing: [[http://portal.acm.org/ft_gateway.cfm?id=1442798&type=pdf&CFID=19884946&CFTOKEN=91282958][Mixed Membership Stochastic Blockmodels]]
---+++ Tue 19 April - Simon Lyons

We will discuss the following papers:
   * Andrieu, Doucet and Tadic: [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.61.2998&rep=rep1&type=pdf][On-Line Parameter Estimation in General State-Space Models]] (sections I and II)
   * Kantas, Doucet, Singh, Maciejowski: [[http://publications.eng.cam.ac.uk/16156/1/sysid09_final_normal_format.pdf][An overview of sequential Monte Carlo methods for parameter estimation in general state-space models]]
---+++ Tue 5 April - Frank Dondelinger

We will discuss the following paper:
   * Brunel and d'Alché-Buc: [[http://www.springerlink.com/content/g152247924145t71/fulltext.pdf][Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks]]
---+++ Tue 22 March - Grigorios Skolidis

We will discuss the following paper:
   * Gianluigi Pillonetto and Francesco Dinuzzo and Giuseppe De Nicolao: [[http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4721437][Bayesian Online Multitask Learning of Gaussian Processes]]
---+++ Tue 8 March - Botond Cseke

We will discuss the following paper:
   * Polson, N. G. & Scott, J. G.: [[http://faculty.chicagobooth.edu/nicholas.polson/research/papers/Bayes1.pdf][Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction]]
---+++ Tue 22 February - Chris Williams

We will discuss the following paper:
   * E C Marshall and D J Spiegelhalter: [[http://ba.stat.cmu.edu/journal/2007/vol02/issue02/marshall.pdf][Identifying outliers in Bayesian hierarchical models: a simulation-based approach]], Bayesian Analysis 2(2) 409-444 (2007)

---+++ Tue 8 February - David Reichert

We will discuss the following paper:
   * <font class="quoted1"><font class="quoted2">Olivier Breuleux, Yoshua Bengio, and Pascal Vincent, Neural Computation (in press): </font></font><font class="quoted1"><font class="quoted2">Quickly Generating Representative Samples from an RBM-Derived Process</font></font>
If there is time, we will also discuss: 
   * <font class="quoted1"><font class="quoted2">Loris Bazzani, Nando de Freitas, Jo-Anne Ting, Deep Learning and Unsupervised Feature Learning NIPS Workshop (2010)</font></font><font class="quoted1"><font class="quoted2">: [[http://deeplearningworkshopnips2010.files.wordpress.com/2010/11/rbmtrack.pdf][<font class="quoted1"><font class="quoted2">Learning attentional mechanisms for simultaneous object tracking and </font></font><font class="quoted1"><font class="quoted2">recognition with deep </font></font><font class="quoted1"><font class="quoted2">networks]]</font></font></font></font>

---+++ Tue 25 January - Charles Sutton

We will discuss the following paper
   * Andrew Gelfand, Yutian Chen, Laurens van der Maaten, Max Welling: [[http://books.nips.cc/papers/files/nips23/NIPS2010_0433.pdf][On Herding and the Perceptron Cycling Theorem]]

---+++ Tue 11 January - "NIPS 2010 highlights"

   * <font color="#ff6347" class="WYSIWYG_COLOR">DR: Le at al.</font><font color="#ff6347" class="WYSIWYG_COLOR">: Tiled convolutional neural networks</font> (*)
   * DR: Larochelle & Hinton: [[http://books.nips.cc/papers/files/nips23/NIPS2010_0824.pdf][Learning to combine foveal glimpses with a third-order Boltzmann machine ]]
   * CW: Ackerman et al [[http://books.nips.cc/papers/files/nips23/NIPS2010_1307.pdf][Towards Property-Based Classification of Clustering Paradigms]]
   * CW: Ranzato et al [[http://books.nips.cc/papers/files/nips23/NIPS2010_1163.pdf][Generating more realistic images using gated MRF's]]
   * IS: Huang et al [[http://books.nips.cc/papers/files/nips23/NIPS2010_1279.pdf][Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression]]
   * IS: Kolter et al [[http://books.nips.cc/papers/files/nips23/NIPS2010_1272.pdf][Energy Disaggregation via Discriminative Sparse Coding]]
   * IM: Bickson and Guestrin [[http://books.nips.cc/papers/files/nips23/NIPS2010_0734.pdf][Inference with Multivariate Heavy-Tails in Linear Models]]
   * FD: Elidan: [[http://books.nips.cc/papers/files/nips23/NIPS2010_0155.pdf][Copula Bayesian Networks]]

(*) DR: Due to my short notice decision to submit something to ICANN, I probably won't have time to look at the paper, so it's up for grabs.

Leave at bottom of list:

CW: There are lots of other papers I like that I hope someone will choose, e.g. Structured Determinantal Point Processes by Alex Kulesza, Ben Taskar; Tree-Structured Stick Breaking for Hierarchical Data, Ryan Adams, Zoubin Ghahramani, Michael Jordan; The Multidimensional Wisdom of Crowds <br />Peter Welinder, Steve Branson, Serge Belongie, Pietro Perona; Self-Paced Learning for Latent Variable Models <br />M. Pawan Kumar, Benjamin Packer, Daphne Koller; Learning Convolutional Feature Hierarchies for Visual Recognition, Kavukcuoglu et al; Divisive Normalization: Justification and Effectiveness as Efficient Coding Transform <br />Siwei Lyu; Global seismic monitoring as probabilistic inference,Nimar Arora, Stuart Russell, Paul Kidwell, Erik Sudderth (application); Energy Disaggregation via Discriminative Sparse Coding, J. Zico Kolter, Siddharth Batra, Andrew Ng (application)

IM: There are some other papers I could say a sentence or two about: "Tree-Structured Stick Breaking for Hierarchical Data" by Ryan Adams, Zoubin Ghahramani, Michael Jordan; "Global seismic monitoring as probabilistic inference" by Nimar Arora, Stuart Russell, Paul Kidwell, Erik Sudderth; "Label Embedding Trees for Large Multi-Class Tasks" by Samy Bengio, Jason Weston, David Grangier; Comparing "Movement extraction by detecting dynamics switches and repetitions" by Silvia Chiappa, Jan Peters and "Mixture of time-warped trajectory models for movement decoding" by Elaine Corbett, Eric Perreault, Konrad Koerding; "Self-Paced Learning for Latent Variable Models" by M. Pawan Kumar, Benjamin Packer, Daphne Koller.
Topic revision: r1 - 22 Jan 2015 - 12:21:23 - Main.s1058681
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