The Machine and Human Intelligence group focuses on probabilistic machine and human learning. We are interested in smart probabilistic algorithms, as implemented by brains and machines, that are robust and sample-efficient — ready to be used "in the wild". We see resource constraints both as a practical necessity and as a useful lever to enforce intelligent behavior. Our research is roughly divided in two complementary goals that inform each other:
- We develop new "smart" machine learning methods, in particular for approximate Bayesian inference.
- We study human probabilistic inference and decision making.
See our Publications page for more detailed information about past and ongoing projects.