It is very exciting to see many interesting papers at ICML this year (see http://icml.cc/2013/?page_id=43 for a list of accepted papers). It is also good to see that several papers are co-authored by the AGBS members.
This year, I have been involved in two ICML papers, both of which are in the area of kernel methods and transfer learning. The first paper is
Domain Generalization via Invariant Feature Representation
K. Muandet (MPI-IS), D. Balduzzi (ETH Zurich), and B. Schoelkopf (MPI-IS)
As opposed to domain adaptation, where one usually assume that the data from the target domain is available during training, domain generalization solves the problem without that assumption by collecting information from several source domains and, given the data from the target domain, infer the target domain during the test time. The paper is already available online (see the link above).
The second paper is
Domain Adaptation under Target and Conditional Shift
K. Zhang (MPI-IS), B. Scoelkopf (MPI-IS), K. Muandet (MPI-IS), and Z. Wang (MPI-IS)
The work investigates the domain adaptation problem when the conditional distribution also changes, as opposed to previous setting where only the marginal can change. We make use of the knowledge from causality to solve this problem. The paper will be available soon.