- Modeling Item-Item Similarities for Personalized Recommendations on Yahoo! Front Page: Agarwal et. al,, Annals of applied Statistics (2011)
- A Flexible, Scalable and Efficient Algorithmic Framework for Primal Graphical Lasso: Mazumder and Agarwal: preprint (2011)

- Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems: Toni et al., JRSI (2009)
- Bayesian design of synthetic biological systems: Barnes, et al., PNAS (2010)

We will discuss the following papers:

- Lei Li, B. Aditya Prakash: Time Series Clustering: Complex is Simpler!. ICML 2011.
- Manuel Gomez Rodriguez, David Balduzzi, Bernhard Schölkopf: Uncovering the Temporal Dynamics of Diffusion Networks. ICML 2011.

We will discuss the following papers:

- K-H Cho, T. Raiko, A. Hin: Enhanced Gradient and Adaptive Learning Rate for Training Restricted Boltzmann Machines. ICML 2011.
- B. Schwehn: Using the Natural Gradient for training Restricted Boltzmann Machines. M.Sc thesis.

We will review the proceedings of UAI 2011. If you would like to discuss a paper, please edit this section to include the title together with your initials.

SL: Pitman-Yor Diffusion Trees - Knowles, Ghahramani

AJS: Sum Product Networks - Poon, Domingos

NH: Bregman divergence as general framework to estimate unnormalized statistical models - Gutmann, Hirayama

CKIW: Conditional Restricted Boltzmann Machines for Structured Output Prediction -- Mnih, Larochelle, Hinton

DR: Classification of Sets using Restricted Boltzmann Machines -- Louradour, Larochelle

- Guest lecture by Matthias Bethge.

- C. Pamminger and S. Fruhwirth-Schnatter, “Model-based clustering of categorical time series,” Bayesian Analysis, vol. 5, no. 2, pp. 345–368, 2010.

- A. U. Asuncion, Q. Liu, A.T. Ihler, P. Smyth: Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. ICML 2010

- FA: A. Vattani, D. Chakrabarti, M. Gurevich: Preserving Personalized Pagerank in Subgraphs
- CW: R. Socher, C. Lin, A. Y. Ng, and C. D. Manning: Parsing Natural Scenes and Natural Language with Recursive Neural Networks
- PRO: X. Zhang, D. Dunson, L. Carin: Tree-Structured Infinite Sparse Factor Model
- IM: Max Welling and Yee Whye Teh: Bayesian learning via stochastic gradient Langevin dynamics
- FD: F. Doshi et al.: Infinite Dynamic Bayesian Networks

We will discuss the following paper:

- Andrew Saxe, pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, Andrew Ng: On Random Weights and Unsupervised Feature Learning, ICML, 2011

We will discuss the following papers:

- Andreas Ruttor, Manfred Opper: Efficient statistical inference for stochastic reaction processes, Phys Rev Lett, 2009
- Andreas Ruttor, Manfred Opper: Approximate parameter inference in a stochastic reaction-diffusion model, Aistats, 2010

Title: Sparse Variational Inference for Multi-Task Learning

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: A conditional game for comparing approximations
- CW:
*Jaakko Peltonen, Samuel Kaski: 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: The Neural Autoregressive Distribution Estimator. (That's fine, IM)
- IM: Two papers on matrix factorization: Lakshminarayanan, Bouchard and Archambeau, Robust Bayesian Matrix Factorisation and Balan, Boyles, Welling, Kim and Park Statistical Optimization of Non-Negative Matrix Factorization

We will discuss the following paper:

- Airoldi, Blei, Fienberg and Xing: Mixed Membership Stochastic Blockmodels

We will discuss the following papers:

- Andrieu, Doucet and Tadic: On-Line Parameter Estimation in General State-Space Models (sections I and II)
- Kantas, Doucet, Singh, Maciejowski: An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

We will discuss the following paper:

- Brunel and d'Alché-Buc: Flow-Based Bayesian Estimation of Nonlinear Differential Equations for Modeling Biological Networks

We will discuss the following paper:

- Gianluigi Pillonetto and Francesco Dinuzzo and Giuseppe De Nicolao: Bayesian Online Multitask Learning of Gaussian Processes

We will discuss the following paper:

- Polson, N. G. & Scott, J. G.: Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction

We will discuss the following paper:

- E C Marshall and D J Spiegelhalter: Identifying outliers in Bayesian hierarchical models: a simulation-based approach, Bayesian Analysis 2(2) 409-444 (2007)

We will discuss the following paper:

- Olivier Breuleux, Yoshua Bengio, and Pascal Vincent, Neural Computation (in press): Quickly Generating Representative Samples from an RBM-Derived Process

- Loris Bazzani, Nando de Freitas, Jo-Anne Ting, Deep Learning and Unsupervised Feature Learning NIPS Workshop (2010): Learning attentional mechanisms for simultaneous object tracking and recognition with deep networks

We will discuss the following paper

- Andrew Gelfand, Yutian Chen, Laurens van der Maaten, Max Welling: On Herding and the Perceptron Cycling Theorem

- DR: Le at al.: Tiled convolutional neural networks (*)
- DR: Larochelle & Hinton: Learning to combine foveal glimpses with a third-order Boltzmann machine
- CW: Ackerman et al Towards Property-Based Classification of Clustering Paradigms
- CW: Ranzato et al Generating more realistic images using gated MRF's
- IS: Huang et al Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression
- IS: Kolter et al Energy Disaggregation via Discriminative Sparse Coding
- IM: Bickson and Guestrin Inference with Multivariate Heavy-Tails in Linear Models
- FD: Elidan: 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

Peter Welinder, Steve Branson, Serge Belongie, Pietro Perona; Self-Paced Learning for Latent Variable Models

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

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.

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