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=1

New 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

• UCL (including Gatsby) http://www.csml.ucl.ac.uk/reading_groups/
• Toronto http://learning.cs.toronto.edu/mlreading.html

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.pdf

Copula 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.pdf

State-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=4524050

Bayesian 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.pdf

Opper, 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 Selection
Gavin 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 Models
Daniel 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