IST Austria

Computer Vision and
Machine Learning Group

Christoph Lampert Christoph H. Lampert
Professor
IST Austria (Institute of Science and Technology Austria)
Coordinates Address: Am Campus 1, IST Austria, 3400 Klosterneuburg, Austria
Email: chl (at) ist (dot) ac (dot) at
Phone: +43 2243 9000 3101 (but sending me email usually works better)

Biographical sketch
Curriculum vitae
Group News
Upcoming Publications and Presentations 08/2017 Two papers accepted to ICML 2017. Congratulations Alex and Asya!

07/2017 CVPR 2017, S.-A. Rebuffi, A. Kolesnikov, G. Sperl, C. H. Lampert. "iCaRL: Incremental Classifier and Representation Learning"
04/2017 AISTATS 2017, A. Zimin, C. H. Lampert. "Learning Theory for Conditional Risk Minimization"
04/2017 ICLR Workshop 2017, G. Martius, C. H. Lampert. "Extrapolation and learning equations"
Team News 04/2017-09/2017 Christoph Lampert will be on a sabbatical at Google Research in Zurich.

03/2017 Georg Martius moved to Tübingen to head his own research group at the Max-Planck Institute for Intelligent Systems. Congratulations!

11/2016 Asya Pentina defended her PhD Thesis "Theoretical Foundations of Multi-task and Lifelong Learning". Congratulations, Dr Pentina!
Recent and Upcoming Activities (see CV for a more complete list)
Workshops and Edited Volumes Workshop: Continuous and Open-Set Learning at CVPR 2017 (with E. Rodner, A. Freytag, T. Boult, J. Denzler)
Edited Volume: Visual Attributes, Springer 2017 (with Rogerio S. Feris and Devi Parikh)
Edited Volume: Advanced Structured Prediction, MIT Press 2015 (with S. Nowozin, P. V. Gehler and J. Jancsary)
Chair Positions and Memberships Area Chair for Conference on Neural Information Processing Systems (NIPS) 2016
Area Chair for European Conference on Computer Vision (ECCV) 2016

Editor for International Journal for Computer Vision (IJCV)
Action Editor for Journal of Machine Learning Research (JMLR)
Associate Editor in Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Member of the Young Academy of the Austrian Academy of Science
Scientific Talks and Posters 10 March 2017: "Incremental Classifier and Representation Learning" at IIT-IST workshop on Transfer Learning, Genoa, IT, 2017
20 January 2017: "The Truth about Artificial Intelligence", Big Think at IST Austria
21 October 2016: "Image Synthesis and Domain Adaptation in Computer Vision". SASHIMI Workshop at MICCAI, Athens, GR, 2016
9 October 2016: "Towards Principled Transfer Learning". TASK-CV Workshop at ECCV, Amsterdam, NL, 2016
27 September 2016: "Multi-task and lifelong learning with unlabeled tasks" Microsoft Research, Cambridge, UK
26 September 2016: "Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation". University of Oxford, UK
9 September 2016: "How to become a Scientist..." and "Computer Vision and Machine Learning at IST Austria" Moscow State University (MSU), Moscow, RU
8 September 2016: "Classifier Adaptation at Prediction Time", Yandex, Moscow, RU
8 September 2016: "Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation" Skoltech, Moscow, RU
7 September 2016: "How to become a Scientist..." and "Computer Vision and Machine Learning at IST Austria" Moscow Institute of Physics and Technology (MIPT), Moscow, RU
6 September 2016: "Multi-task learning with unlabeled tasks" Higher School of Economics, Moscow, RU
External Teaching 5-9 September 2016: Higher School of Economics, Moscow, RU "Probabilistic Graphical Models" Slides: 23 August 2016: Vision and Sport Summer School, Prague, CZ. "Machine Learning for Computer Vision" (talk slides PDF exercise data: ZIP)
Teaching at IST Austria Q3 2016/17: "Data Science and Scientific Computing" (track core course)
Q2 2016/17: "Probabilistic Graphical Models" (advanced course)
Q1 2016/17: "Computer Vision and Machine Learning" (Introduction to Research at IST Austria)
Q3 2015/16: "Data Science and Scientific Computing" (track core course)
Q3 2015/16: "Statistical Machine Learning" (advanced course)
Q1/Q2 2015/16: Project Course: "Computer Vision and Machine Learning" (advanced course, project format)