Probabilistic Inference Group (PIGS) - Archive
Meetings in 2012
27 November: Amos Storkey
"Reconceiving Machine Learning", Bob Williamson et al.
http://users.cecs.anu.edu.au/~williams/DPProposal.pdf"Machine Learning that Matters", Kiri Wagstaff
http://icml.cc/2012/papers/298.pdf
13 November: Ali Eslami
"Improving neural networks by preventing co-adaptation of feature detectors"
G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever and R. R. Salakhutdinov
http://www.cs.toronto.edu/~hinton/absps/dropout.pdf"Multiresolution Gaussian Processes"
E. B. Fox and D. B. Dunson
NIPS 2012
http://stat.duke.edu/sites/default/files/papers/2012-11.pdf
30 October: Simon Lyons
The scaled unscented transformation
S.J. Julier
Proceedings of the American Control Conference (2002)
http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1025369&tag=1New extension of the Kalman filter to nonlinear systems
S.J. Julier, J.K. Uhlmann
Signal Processing, Sensor Fusion, and Target Recognition (1997)
http://proceedings.spiedigitallibrary.org/proceeding.aspx?articleid=925842
Schedule
Please fill in your name or contact
Krzysztof Geras, if you would like to present and discuss papers in a specific research area of machine learning.
Paper Recommendations
Please add papers you consider appropriate for
PIGS. Please also add thematic categories that are not covered.
Deep learning and Energy Based Models
Non-Parametrics
Variational Methods
Sampling Methods
Dynamical Systems
Information Theory
Other Journal Clubs
Previous meetings
16 October: Krzysztof Geras
No Unbiased Estimator of the Variance of K-Fold Cross-Validation (JMLR 2004)
Yoshua Bengio, Yves Grandvalet
http://www.jmlr.org/papers/volume5/grandvalet04a/grandvalet04a.pdf
2 October: Benigno Uria
2010 Memisevic, R., Hinton, G.
Learning to Represent Spatial Transformations with Factored
Higher-Order Boltzmann Machines.
Neural Computation June 2010, Vol. 22, No. 6: 1473-1492.
http://www.cs.toronto.edu/~rfm/pubs/factored.pdf
18 September: Peter Orchard
Lightning-speed Structure Learning of Nonlinear Continuous Networks
http://jmlr.csail.mit.edu/proceedings/papers/v22/elidan12b/elidan12b.pdfCopula Network Classifiers (CNCs)
http://jmlr.csail.mit.edu/proceedings/papers/v22/elidan12a/elidan12a.pdf
4 September: Guido Sanguinetti
P. Diggle et al, Geostatistical inference under preferential sampling,
JRSS C (Appl. Stat.) 59 (2010)
http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9876.2009.00701.x/abstract
21 August: Chris Williams
Bayesian Model Checking and Model Diagnostics - Hal S. Stern and Sandip Sinharay
Handbook of Statistics, Vol. 25 (2005)
and
Induction and deduction in Bayesian data analysis, Andrew Gelman, RMM vol 2, 2011 67-78
19th July: Ioan Stanculescu
Ioan will present the following papers:
Bayesian Conditional Cointegration - Chris Bracegirdle and David Barber
http://icml.cc/2012/papers/570.pdfState-Space Inference and Learning with Gaussian Processes - Ryan Turner, Marc Deisenroth and Carl Rasmussen
http://jmlr.csail.mit.edu/proceedings/papers/v9/turner10a/turner10a.pdf
19th July: Yichuan Zhang
Yichuan will present the following paper:
Accelerated Adaptive Markov Chain for Partition Function Computation - S. Ermon, C. P. Gomes, A. Sabharwal, B. Selman (NIPS 2011)
15th May: AISTATS review
Please add your initials below together with a link to the paper you wish to present.
IS:
Approximate Inference in Additive Factorial HMMs with Application to Energy Disaggregation - J. Zico Kolter, Tommi Jaakkola
CS:
Lightning-speed Structure Learning of Nonlinear Continuous Networks - Gal Elidan
KG:
Learning from Weak Teachers - Ruth Urner, Shai Ben-David and Ohad Shamir
PO:
Causality with Gates - John Winn
CW:
Deep Boltzmann Machines as Feed-Forward Hierarchies -- Montavon, Braun, Mueller
AS:
Classifier Cascade for Minimizing Feature Evaluation Cost -- Minmin Chen, Zhixiang Xu, Kilian Weinberger, Olivier Chapelle, Dor Kedem
1st May: Andrea Ocone
Andrea will discuss the following paper:
Designing attractive models via automated identification ofchaotic and oscillatory dynamical regimes: Silk D, Kirk PD, Barnes CP, Toni T, Rose A, Moon S, Dallman MJ, Stumpf
17th April: Simon Lyons
Simon will discuss the following papers:
Bayesian Compressive Sensing: S. Ji, Y. Xue, L. Carin
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4524050Bayesian Compressive Sensing Via Belief Propagation: D. Baron , S. Sarvotham , R. G. Baraniuk
http://webee.technion.ac.il/people/drorb/pdf/CSBP012010.pdf
3rd April: Botond Cseke
Botond will discuss the following papers:
Opper, Paquet, Winther: Improving on Expectation Propagation
http://www.ulrichpaquet.com/Papers/ImprovingOnEP.pdfOpper, Paquet, Winther: Cumulant expansions for improved inference with EP in discrete Bayesian networks
http://las.ethz.ch/discml/papers/opper11cumulant.pdf
20 March: Chris Williams
Chris will discuss the following paper:
Conditional Likelihood Maximisation: A Unifying Framework for Information Theoretic Feature SelectionGavin Brown, Adam Pocock, Ming-Jie Zhao, Mikel Luján;
13(Jan):27−66, 2012.
6 March: Charles Sutton
Charles will discuss the following paper:
A Spectral Algorithm for Learning Hidden Markov ModelsDaniel Hsu, Sham M. Kakade, Tong Zhang
21 February: Iain Murray
Iain will discuss the following paper:
Statistical Tests for Optimization Efficiency: L. Boyles, A. Korattikara, D. Ramanan, M. Welling (NIPS 2011)
7 February: Jono Millin
Jono will present the following papers:
Bayesian Bias Mitigation for Crowdsourcing: Fabian L. Wauthier, Michael I. Jordan. Proceedings of NIPS (2011)
http://books.nips.cc/papers/files/nips24/NIPS2011_1021.pdf
A Collaborative Mechanism for Crowdsourcing Prediction Problem: Jacob D. Abernethy, Rafael M. Frongillo, Proceedings of NIPS (2011)
http://books.nips.cc/papers/files/nips24/NIPS2011_1403.pdf
24 January: Amos Storkey
Amos will present the following paper:
Sparse Bayesian Multi-Task Learning, C. Archambeau, S. Guo, O. Zoeter, NIPS 2011.
Amos will discuss this paper with reference to other Bayesian approaches to sparsity. For example:
Bayesian Inference and Optimal Design in the Sparse Linear Model, M. Seeger, JMLR 2008
10 January: NIPS Review
CW:
Object Detection with Grammar Models - Ross B. Girshick, Pedro Felzenszwalb, David Mcallester
DR:
Learning to Learn with Compound HD Models - Ruslan R. Salakhutdinov, Josh Tenenbaum, Antonio Torralba
BU:
Selecting Receptive Fields in Deep Networks - Adam Coates, Andrew Y. Ng
PO:
Variational Gaussian Process Dynamical Systems -
Andreas C. Damianou, Michalis Titsias, Neil D. Lawrence
(mention)
Sparse Inverse Covariance Estimation Using Quadratic Approximation -
Cho-Jui Hsieh, Matyas A. Sustik, Inderjit S. Dhillon, Pradeep K. Ravikumar
CS:
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent Benjamin Recht, Christopher Re, Stephen Wright, Feng Niu. I also liked
Learning unbelievable probabilities Xaq S. Pitkow, Yashar Ahmadian, Ken D. Miller
KG:
Co-Training for Domain Adaptation - Minmin Chen, Kilian Q. Weinberger, John Blitzer