| PI: Christoph H. Lampert (he/him)|
ISTA (Institute of Science and Technology Austria)
Address: Am Campus 1, ISTA, 3400 Klosterneuburg, Austria
Email: chl (at) ist (dot) ac (dot) at
Phone: +43 2243 9000 3101 (but sending me email usually works better)
10/2023 A paper accepted to TMLR. Congratulations Jonny and Michelle!
06/2023 Our alumnus Alexander Kolesnikov received the ISTA Alumni Award. Congratulation, Alex!
04/2023 A paper accepted to CVPR. Congratulations Alex!
01/2023 A paper accepted to ICLR. Congratulations Alex!
09/2022 ISTA was hacked! Expect some IT-related hiccups over the next few
|Recent Publications and Preprints||
Recently on arXiv: Denis Kuznedelev, Eldar Kurtic, Eugenia Iofinova, Elias Frantar, Alexandra Peste, Dan Alistarh. "Accurate Neural Network Pruning Requires Rethinking Sparse Optimization".
Recently on arXiv: Jonathan Scott, Hossein Zakerinia, Christoph H. Lampert.
"PeFLL: A Lifelong Learning Approach to Personalized Federated Learning".
Recently on arXiv: Mher Safaryan, Alexandra Peste, Dan Alistarh.
"Knowledge Distillation Performs Partial Variance Reduction".
arXiv: 2305.17581 [cs.LG]
Recently on arXiv: Peter Súkeník, Marco Mondelli, Christoph H. Lampert.
"Deep Neural Collapse Is Provably Optimal for the Deep Unconstrained Features Model
". arXiv:2305.13165 [cs.LG]
Recently on arXiv: Eugenia Iofinova, Alexandra Peste, Dan Alistarh. "Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures".
arXiv:2304.12622 [cs.LG, cs.CV]
Recently on arXiv: Jonathan Scott, Michelle Yeo, Christoph H. Lampert.
"Cross-client Label Propagation for Transductive Federated Learning
". arXiv:2210.06434 [cs.LG]
Recently on arXiv: Peter Súkeník, Christoph H. Lampert.
"Generalization In Multi-Objective Machine Learning". arXiv:2208.13499 [cs.LG]
06/2023 CVPR 2023. Eugenia Iofinova, Alexandra Peste, Dan Alistarh. "Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures" 05/2023 ICLR 2023. Alexandra Peste, Adrian Vladu, Christoph H. Lampert, Dan Alistarh. "CrAM: A Compression-Aware Minimizer" 09/2022 TMLR. Eugenia Iofinova*, Nikola Konstantinov*, Christoph H. Lampert. "FLEA: Provably Robust Fair Multisource Learning from Unreliable Training Data" 10/2022 ECCV 2022. Bernd Prach, Christoph H. Lampert. "Almost-Orthogonal Layers for Efficient General-Purpose Lipschitz Networks" 08/2022 ICPR 2022. Paulina Tomaszewska, Christoph H. Lampert. "Lightweight Conditional Model Extrapolation for Streaming Data under Class-Prior Shift" 07/2022 JMLR. Nikola Konstantinov, Christoph H. Lampert. "Fairness-Aware PAC Learning from Corrupted Data"
09/2023 Hossein passed his Qualifying Exam. Congratulations!
07/2023 Egor Zverev and Nikita Kalinin joined our group. Welcome!
04/2023 Alex Peste defended her PhD thesis "Robustness and Fairness in Machine Learning Learning". Congratulations, Dr Peste!
03/2023 Peter passed his Qualifying Exam. Congratulations!
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
Action Editor for Journal of Machine Learning Research (JMLR)
(until 2023) Editor for International Journal for Computer Vision (IJCV)
(until 2022) Associate Editor in Chief for IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
|External Talks||21 Sep 2020: Federated Learning -- All for One and One for All, Invited talk at GCPR 2023, Heidelberg, Germany 12 Jun 2023: Robust Learning from Multiple Sources, ISOR Seminar, University of Viena 05 Jun 2023: Trustworthy Machine Learning: The Quest for Robustness, Austrian Computer Science Day, TU Graz, Austria 27 Oct 2022: Robust and Fair Multi-Source Learning, The Mathematics of Machine Learning Workshop, BCAM Bilbao 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|
|External Teaching||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)|
|Teaching and other presentations
10/2022 "Modern Machine Learning" (coming up)
09/2021 Intro to the "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)