Relevant References

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Background

Given the tight schedule, the working assumption of the workshop will be that some Monte Carlo basics will be common knowledge. That is, participants are expected to know "textbook" material. Please check that you are familiar with the bulk of the material in at least one of the following:

• Chapters 29-30 of MacKay's book: http://www.inference.phy.cam.ac.uk/mackay/itila/book.html
• Chapter 11 of Bishop's book: http://research.microsoft.com/en-us/um/people/cmbishop/PRML/
• Neal's tome: http://www.cs.toronto.edu/~radford/review.abstract.html

…or similar (please do add other alternatives if you have them).

Papers specifically on practical Monte Carlo methods

TODO

Monte Carlo papers at recent machine learning meetings

The following recent papers either proposed novel Monte Carlo methodology, or made significant use of Monte Carlo methods in an application.

NIPS 23

• Paolo Viappiani and Craig Boutilier. Optimal Bayesian Recommendation Sets and Myopically Optimal Choice Query Sets (Theoretical and practical results on Bayesian utility elicitation. Monte Carlo methods are used in the experiments, but not crucial for the main results. I am planning to investigate more the efficiency of Monte Carlo methods in this domain)

NIPS 22

• Randomized Pruning: Efficiently Calculating Expectations in Large Dynamic Programs. Alexandre Bouchard-Côté, Slav Petrov, Dan Klein
• A Stochastic approximation method for inference in probabilistic graphical models. Peter Carbonetto, Matthew King, Firas Hamze
• FACTORIE: Probabilistic Programming via Imperatively Defined Factor Graphs. Andrew McCallum, Karl Schultz, Sameer Singh
• Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process. Finale Doshi-Velez, David Knowles, Shakir Mohamed, Zoubin Ghahramani
• Learning in Markov Random Fields using Tempered Transitions. Ruslan Salakhutdinov

AISTATS 2010

• Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines. Guillaume Desjardins, Aaron Courville, Yoshua Bengio, Pascal Vincent, Olivier Delalleau
• Parallelizable Sampling of Markov Random Fields. James Martens, Ilya Sutskever
• Elliptical slice sampling. Iain Murray, Ryan Adams, David MacKay
• Approximation of hidden Markov models by mixtures of experts with application to particle filtering. Jimmy Olsson, Jonas Ströjby
• A generalization of the Multiple-try Metropolis algorithm for Bayesian estimation and model selection. Silvia Pandolfi, Francesco Bartolucci, Nial Friel
• A Markov-Chain Monte Carlo Approach to Simultaneous Localization and Mapping. Peter Torma, András György, Csaba Szepesvári
• Sequential Monte Carlo Samplers for Dirichlet Process Mixtures. Yener Ulker, Bilge Günsel, Taylan Cemgil
• A highly efficient blocked Gibbs sampler reconstruction of multidimensional NMR spectra. Ji Won Yoon, Simon Wilson, K. Hun Mok

ICML 2010

• Restricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate. Phil Long, Rocco Servedio
• Particle Filtered MCMC-MLE with Connections to Contrastive Divergence. Arthur Asuncion, Qiang Liu, Alex Ihler, Padhraic Smyth
• Learning Deep Boltzmann Machines using Adaptive MCMC. Ruslan Salakhutdinov

NIPS 21

• The Gaussian Process Density Sampler. Ryan Adams, Iain Murray, David MacKay
• Particle Filter-based Policy Gradient in POMDPs. Pierre-Arnaud Coquelin, Romain Deguest, Remi Munos
• An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering. Dilan Gorur, Yee Whye Teh
• Evaluating probabilities under high-dimensional latent variable models. Iain Murray, Ruslan Salakhutdinov