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

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!

Five Papers at ICML 2014


The HIPS group co-authored five papers to appear at this year's International Conference on Machine Learning (ICML).

Recent Publications