The problems studied in the department can be subsumed under the
heading of empirical inference, i.e., inference
performed on the basis of empirical data. This includes statistical learning,
but also the inference of
causal structures from statistical data, leading to models that
provide insight into the underlying mechanisms, and make predictions
about the effect of interventions. Likewise, the type of empirical
data can vary, ranging from biological measurements (e.g., in neuroscience) to astronomical observations. We are conducting
theoretical, algorithmic, and experimental studies to try and
understand the problem of empirical inference.
The department was started around statistical learning theory and kernel methods. It has since broadened
its set of inference tools to include a stronger component of
Bayesian methods, including graphical models with a recent focus on
issues of causality. In terms of the inference tasks being studied,
we have moved towards tasks that go beyond the relatively
well-studied problem of supervised learning, such as semi-supervised
learning or transfer learning. Finally, we have continuously
striven to analyze challenging datasets from biology, astronomy, and
other domains, leading to the inclusion of several application areas
in our portfolio.
No matter whether the
applications are done in the department or in collaboration with
external partners, considering a whole range of applications helps
us study principles and methods of inference, rather than
inference applied to one specific problem domain.
The most competitive publication venues in empirical inference are NIPS (Neural Information Processing Systems), ICML (International Conference on Machine Learning), UAI (Uncertainty in Artificial Intelligence), and for theoretical work, COLT (Conference on Learning Theory). The presence at these conferences makes us one of the top international machine learning labs. In addition, we sometimes submit our work to the leading application oriented conferences in neighboring fields including computer vision (ICCV, ECCV, CVPR) and data mining (KDD, ICDM, SDM), as well as to specialized journals.
Our work has earned us a number of awards, including best paper prizes at COLT 2003, NIPS 2004, COLT 2005, COLT 2006, ICML 2006, ALT 2007, DAGM 2008, CVPR 2008, ISMB 2008, ECCV 2008, NIPS 2008, NIPS 2009, UAI 2010, ICINCO 2010, DAGM 2011, IROS 2012, NIPS 2013, CIP 2014, and honorable mentions at IROS 2008, NIPS 2009, DAGM 2009, KDD 2010, ECML/PKDD 2011, and IROS 2012.
Empirical Inference (2010-2015)