- 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.

This topic: ANC > PIGSTwoThousandAndEleven

Topic revision: r1 - 22 Jan 2015 - 12:21:23 - Main.s1058681

Copyright © by the contributing authors. All material on this collaboration platform is the property of the contributing authors.

Ideas, requests, problems regarding TWiki? Send feedback

This Wiki uses Cookies

Ideas, requests, problems regarding TWiki? Send feedback

This Wiki uses Cookies