"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

"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

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.

Please add papers you consider appropriate for PIGS. Please also add thematic categories that are not covered.

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

Yoshua Bengio, Yves Grandvalet

http://www.jmlr.org/papers/volume5/grandvalet04a/grandvalet04a.pdf

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

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

JRSS C (Appl. Stat.) 59 (2010) http://onlinelibrary.wiley.com/doi/10.1111/j.1467-9876.2009.00701.x/abstract

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

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

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)

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

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

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

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

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.

Charles will discuss the following paper:

A Spectral Algorithm for Learning Hidden Markov Models

Daniel Hsu, Sham M. Kakade, Tong Zhang

Iain will discuss the following paper:

Statistical Tests for Optimization Efficiency: L. Boyles, A. Korattikara, D. Ramanan, M. Welling (NIPS 2011)

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

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

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

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