| Christoph H. Lampert (he/him)|
IST Austria (Institute of Science and Technology Austria)
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)
07/2022 A paper accepted to ECCV. Congratulations Bernd!
04/2022 A paper accepted to JMLR. Congratulations Niko!
03/2022 A paper accepted to ICPR. Congratulations Paulina!
11/2021 Two papers accepted to NeurIPS Workshops. Congratulations Alex and Niko!
10/2021 A paper accepted to IEEE BigData Special Session "Machine Learning for BigData". Congratulations Jasmin!
01/2021 A paper accepted to ICLR. Congratulations Mary!
12/2020 A paper accepted to NeurIPS. Congratulations Paul!
08/2020 A paper accepted to GCPR. Congratulations Vaclav!
07/2020 Niko presented his paper On the Sample Complexity of Adversarial Multi-Source PAC Learning at ICML 2020
06/2020 A paper accepted to ICML. Congratulations Niko!
04/2020 Mary presented her paper Functional vs. parametric equivalence of ReLU networks at (virtual) ICLR.
02/2020 A paper accepted to CVPR. Congratulations Paul!
12/2019 A paper accepted to ICLR. Congratulations Mary!
12/2019 IST Austria has been approved as ELLIS unit.
09/2019 Asya Pentina (PhD 2016) received an ELLIS Ph.D. award for "outstanding research achievements during the dissertation phase in artificial intelligence and machine learning". Congratulations!
|Recent Publications and Preprints||
Recently on arXiv: Eugenia Iofinova, Nikola Konstantinov, Christoph H. Lampert.
"FLEA: Provably Fair Multisource Learning from Unreliable Training Data". arXiv:2106.11732 [cs.LG]
Recently on arXiv: Paul Henderson, Christoph H. Lampert, Bernd Bickel.
"Unsupervised Video Prediction from a Single Frame by Estimating 3D Dynamic Scene Structure". arXiv:2106.09051 [cs.CV]
12/2021 IEEE BigData 2021 (Special Session MLBD). Jasmin Lampert, Christoph H. Lampert. "Overcoming Rare-Language Discrimination in Multi-Lingual Sentiment Analysis"
05/2021 ICLR 2021.
Mary Phuong, Christoph H. Lampert. "The inductive bias of ReLU networks on orthogonally separable data"
12/2020 NeurIPS 2020. Paul Henderson,
Christoph H. Lampert. "Unsupervised object-centric video generation and decomposition in 3D"
08/2020 GCPR 2020. Vaclav Volhejn, Christoph H. Lampert. "Does SGD Implicitly Optimize for Smoothness?"
07/2020 ICML 2020. Nikola Konstantinov, Elias Frantar, Dan Alistarh, Christoph H. Lampert.
"On the Sample Complexity of Adversarial Multi-Source PAC Learning".
07/2020 ICML Workshop "Object-Oriented Learning". Titas Anciukevicius, Christoph H. Lampert, Paul Henderson.
"Structured Generative Modeling of Images with Object Depths and Locations",
06/2020 CVPR 2020. Paul Henderson, Vagia Tsiminaki, Christoph H. Lampert. "Leveraging 2D Data to Learn Textured 3D Mesh Generation".
04/2020 ICLR 2020. Mary Phuong, Christoph H. Lampert.
"Functional vs. parametric equivalence of ReLU networks "
03/2020 WACV 2020. Amelie Royer, Christoph H. Lampert.
" Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios".
03/2020 WACV 2020. Amelie Royer, Christoph H. Lampert.
" A Flexible Selection Scheme for Minimum-Effort Transfer Learning".
12/2019 NeurIPS 2019 Workshop "ML with Guarantees". Anastasia Pentina, Christoph H. Lampert. "Multi-source domain adaptation with guarantees".
10/2019 IJCV. Rémy Sun, Christoph H. Lampert.
"KS(conf): A Light-Weight Test if a Multiclass Classifier Operates Outside of Its Specifications"
10/2019 IJCV. Paul Henderson, Vittorio Ferrari.
Learning Single-Image 3D Reconstruction by Generative Modelling of Shape, Pose and Shading
07/2019 ICCV 2019. Mary Phuong, Christoph H. Lampert. "Distillation-Based Training for Multi-Exit Architectures"
06/2019 ICML 2019 Workshop on Adaptive & Multitask Learning. Alexander Zimin, Christoph H. Lampert. "Tasks Without Borders: A New Approach to Online Multi-Task Learning".
06/2019 ICML 2019. Nikola Konstantinov, Christoph H. Lampert. "Robust Learning from Untrusted Sources". 06/2019 ICML 2019. Mary Phuong, Christoph H. Lampert. "Towards Understanding Knowledge Distillation".
02/2022 Niko Konstantinov defended his PhD thesis "Robustness and Fairness in Machine Learning Learning". Congratulations, Dr Konstantinov!
01/2022 Jonny passed his Qualifying Exam. Congratulations!
05/2021 Mary Phuong defended her PhD thesis "Underspecification in Deep Learning". Congratulations, Dr Phuong!
05/2021 Jonny Scott affiliated with our group. Welcome, Jonny!
03/2021 Bernd passed his Qualifying Exam. Congratulations!
08/2020 Amelie Royer defended her PhD thesis "Leveraging structure in Computer Vision tasks for flexible Deep Learning models". Congratulations, Dr Royer!
07/2020 Bernd Prach affiliated with our group. Welcome, Bernd!
01/2020 Alex Peste passed her Qualifying Exam. Congratulations!
09/2018 Alex Zimin defended his PhD thesis "Learning from dependent data". Congratulations, Dr Zimin!
02/2018 Alex Kolesnikov defended his PhD thesis "Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images". Congratulations, Dr Kolesnikov!
|Recent and Upcoming Activities (see CV for a more complete list)|
|Workshops, Books and Edited Volumes||
Edited Book: Wie Maschinen Lernen, Springer 2019 (with Kristian Kersting and Constantin Rothkopf)
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||
ELLIS Fellow and Unit Director
Associate Editor in Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
Action Editor for Journal of Machine Learning Research (JMLR)
Editor for International Journal for Computer Vision (IJCV)
|External Talks||17 August 2022: "Behind the Scenes: How Does One Become a (Machine Learning) Researcher and What Does It Mean To Be One?" Estonian Summer School on Computer and Systems Science, Tartu, EE. 14 Apr 2022: Robust Learning from Multiple Sources, Mathematical Machine Learning Seminar MPI Mis + UCLA 23 Jul 2021: Lifelong and Meta-Learning: Beyond Just More of the Same, ICML2021 Workshop on Continual Learning, online event 17 Dec 2020: Robust Learning from Multiple Sources, Invited Talk at the Workshop of the ELLIS Program "Interactive Learning and Interventional Representations" (ILIR), online event 28 Sep 2020: Robust Learning from Multiple Sources, Invited Talk at GCPR 2020, online event (video) 17 Jul 2020: Learning Theory for Continual and Meta-Learning, ICML2020 Workshop on Continual Learning, online event 2 July 2020: Learning Theory for Continual and Meta-Learning, Sheffield Machine Learning Seminar (online) 2 Mar 2020: Efficient and Adaptive Models for Visual Scene Analysis, Opening of the CD-Laboratory for Embedded Machine Learning, Vienna 21 Jan 2020: Efficient and Adaptive Models for Visual Scene Analysis, Northern Lights Deep Learning Workshop, Tromso|
16/17 August 2022: Estonian Summer School on Computer and Systems Science, Tartu, EE.
"Robust and Fair Machine Learning" part 1 (PDF) part 2 (PDF)
26 July 2022: Vision and Sport Summer School, Prague, CZ. Part 1 (PDF) Part 2 (PDF) exercise sheet (PDF) exercise data (ZIP)
25 September 2019: IWRSchool Heidelberg "Transfer Learning" (talk slides PDF)
28 November 2018: Vienna Graduate School on Computational Optimization, TU Vienna, AT. "Algorithmic Stability and Generalization"- (talk slides PDF)
22 August 2018: Vision and Sport Summer School, Prague, CZ. "Machine Learning for Computer Vision"- (talk slides PDF exercise data: ZIP)
|Teaching and other presentations
at IST Austria
09/2021 "Machine Learning and Computer Vision" research group
09/2021 "Intro to DSSC Track for Graduate Students"
07/2021 "Intro to DSSC Track for ISTerns"
Q4(moved!) 2020/21: "Concentration of Measure" (advanced course, with Jan Maas)
Q3(moved!) 2020/21: "Probabilistic Graphical Models" (advanced course, with Paul Henderson)
Q1 2020/21: "Statistical Machine Learning" (advanced course)
Q3 2019/20: "Formal Methods for Learned Systems" (seminar)