Building Intelligent Probabilistic Systems

In the HIPS group, we are interested in building intelligent algorithms. What makes a system intelligent? Our philosophy is that "intelligence" means making decisions under uncertainty, adapting to experience, and discovering structure in high-dimensional noisy data. The unifying theme for research in these areas is developing new approaches to statistical inference: uncovering the coherent structure that we cannot directly observe and using it for exploration and to make decisions or predictions. We develop new models for data, new tools for performing inference, and new computational structures for representing knowledge and uncertainty.

Recent News

Bayesian Optimization Workshop at NIPS

 

At NIPS this year will be a workshop on Bayesian Optimization in Academia and Industry, on Friday 12 December 2014. The announcement is below. We invite abstracts, due on October 23, 2014.

New Spearmint Release

 

After many months of updates and new research, we're announcing a completely updated version of Spearmint, our tool for Bayesian optimization. It is available for use under a non-commercial license. This is a long-term collaboration between Jasper Snoek, Kevin Swersky, Hugo Larochelle, Michael Gelbart, and Ryan Adams.

MICMAT: Python Scientific Computing on Intel Xeon Phi

 

Oren Rippel has recently released the first version of his new Python library MICMAT. This library has a similar functionality to cudamat, but is focused on Intel's newXeon Phi (MIC) architecture, rather than nVidia's CUDA.

"Firefly Monte Carlo" Wins Best Paper at UAI

 

Dougal Maclaurin's paper Firefly Monte Carlo: Exact MCMC with Subset of Data has won the Microsoft Best Paper Award at this year's Conference on Uncertainty in Artificial Intelligence (UAI). Congrats Dougal!

Recent Publications

Parallel MCMC with Generalized Elliptical Slice Sampling. Nishihara, R, Murray I, Adams RP.  Journal of Machine Learning Research (JMLR). 2014.   { PDF }
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 }
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 }
Bayesian Optimization with Unknown Constraints. Gelbart, MA, Snoek J, Adams RP.  Thirtieth Conference on Uncertainty in Artificial Intelligence (UAI). 2014.   { arXiv:1403.5607 [stat.ML] | PDF | Code }