New Computational Tools for Statistical Inference

Powerful mathematical models and representations are only useful if we can perform the computation necessary to manipulate them. In the context of intelligent probabilistic systems, this can often be viewed as the problem of performing statistical inference. We are interested in building new computational tools that enable this inference, most often by developing new Monte Carlo methods, with potential impact both within computer science and statistics, but also across the broader sciences, such as biology and physics.
Accelerating MCMC via Parallel Predictive Prefetching. Angelino, E, Kohler E, Waterland A, Seltzer M, Adams RP.  Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI). 2014.   { arXiv:1403.7265 [stat.ML] | PDF | Code }
Firefly Monte Carlo: Exact MCMC with Subsets of Data. Maclaurin, D, Adams RP.  Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI). 2014.   { arXiv:1403.5693 [stat.ML] | PDF }
Training Restricted Boltzmann Machines on Word Observations. Dahl, GE, Adams RP, Larochelle H.  Proceedings of the 29th International Conference on Machine Learning. 2012.   { arXiv:1202.5695 [cs.LG] | PDF }
Slice Sampling Covariance Hyperparameters in Latent Gaussian Models. Murray, I, Adams RP.  Advances in Neural Information Processing Systems 23. 1723-1731. 2010.   { arXiv:1006.0868 [stat.CO] | PDF | Code }
Elliptical Slice Sampling. Murray, I, Adams RP, MacKay DJC.  Journal of Machine Learning Research: Workshop and Conference Proceedings (AISTATS). 9:541-548. 2010.   { arXiv:1001.0175 [stat.CO] | PDF | Code }