DeepLearn 2023 Spring
9th International School
on Deep Learning
Bari, Italy · April 03-07, 2023
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Christoph Lampert

Christoph Lampert

Institute of Science and Technology Austria

[intermediate] Training with Fairness and Robustness Guarantees

Summary

In times where more and more automatic decision systems are deployed for real-world tasks, machine learning researchers have to make sure that the systems they create are not only accurate but also robust and fair in the decisions they make. The lectures will give an introduction what these terms mean scientifically and will discuss recent developments for achieving them.

Syllabus

  • Introduction to classifier robustness
  • How to make a classifier robust
  • Introduction to algorithmic fairness
  • How to make a classifier fair
  • Robust fair learning from unreliable data

References

Solon Barocas, Moritz Hardt, Arvind Narayanan. “Fairness and Machine Learning: Limitations and Opportunities”, https://fairmlbook.org/

Nikola Konstantinov. “Fairness and Robustness in Machine Learning”. PhD Thesis, IST Austria (2022). https://shorturl.at/iyP16

Pre-requisites

Elementary concepts of linear algebra (vectors, matrices, norms). Elementary concepts of calculus (continuity, derivatives, integrals). Elementary concepts of probability (random variables, conditional probability, expected value). Elementary concepts of machine learning (classification, loss functions, empirical risk minimization, held-out data for evaluation).

Short bio

Christoph Lampert received the PhD degree in mathematics from the University of Bonn in 2003. Afterwards he held postdoctoral positions at the German Research Center for Artificial Intelligence and the Max-Planck Institute for Biological Cybernetics. In 2010 he joined the Institute of Science and Technology Austria (ISTA) first as an Assistant Professor and since 2015 as a Professor. There, he leads the research group for Machine Learning and Computer Vision and since 2019 he is also the director of ISTA’s ELLIS unit. His research on machine learning and computer vision has won international and national awards, including a best paper prize at CVPR 2008. In 2012 he was awarded an ERC Starting Grant (consolidator phase) by the European Research Council. In 2019 and 2021 he was a member of the ERC Starting Grant evaluation panel. He is a former Associate Editor in Chief of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) and a current member of the editorial board of the International Journal of Computer Vision (IJCV) and the Journal for Machine Learning Research (JMLR).

Other Courses

Babak Ehteshami BejnordiBabak Ehteshami Bejnordi
speakers-gleyzerSergei V. Gleyzer
speakers-kumarVipin Kumar
speakers-goldbergerJacob Goldberger
speakers-jingbianYingbin Liang
Xiaoming LiuXiaoming Liu
Michael MahoneyMichael Mahoney
Liza MijovicLiza Mijovic
William S. NobleWilliam S. Noble
Bhiksha RajBhiksha Raj
Holger Rauhut‪Holger Rauhut
Bart ter Haar RomenyBart ter Haar Romeny
Tara SainathTara Sainath
Martin SchultzMartin Schultz
Adi Laurentiu TarcaAdi Laurentiu Tarca
Emma TolleyEmma Tolley
Michalis VazirgiannisMichalis Vazirgiannis
Atlas WangAtlas Wang
Guo-Wei WeiGuo-Wei Wei
Lei XingLei Xing
Xiaowei XuXiaowei Xu

DeepLearn 2023 Spring

CO-ORGANIZERS

Department of Computer Science
University of Bari “Aldo Moro”

Institute for Research Development, Training and Advice – IRDTA, Brussels/London

Active links
  • DeepLearn 2023 Summer – 10th International Gran Canaria School on Deep Learning
  • BigDat 2023 Summer – 7th International School on Big Data

Photos by: Ph. Eufemia Lella

Past links
  • DeepLearn 2023 Winter
  • DeepLearn 2022 Autumn
  • DeepLearn 2022 Summer
  • DeepLearn 2022 Spring
  • DeepLearn 2021 Summer
  • DeepLearn 2019
  • DeepLearn 2018
  • DeepLearn 2017
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