Research Interests (Future)

In the future, I would like to explore several direction at the intersection of Algorithmic Fairness and AutoML. If you are interested in collaborating, get in contact!

There are several areas I’d like to do research on in the future:

• L(L)M’s / Foundation models for data beyond text and image
• Interactive and Fairness-aware AutoML systems
• Causality- & fairness-aware AutoML
• Integrating AutoML and Causality
• Better and more realistic benchmarks for asynchronous & noisy HPO/NAS

Previous Work

Throughout my PhD I conducted research on several topics with the overarching goal to robustify, simplify and improve machine learning for non-expert users. This website only shows a selected subset of my work, please refer to my google scholar profile for an up-to-date and more comprehensive overview.

AutoML

Abstract: My interests in AutoML are mainly in meta-learning and hyperparameter optimization (HPO). The goal there is to improve existing HPO optimizers or to figure out if (and how) we can use results from previous experiments to jumpstart optimization on a new dataset. I am similarly interested in developing AutoML beyond its current scope, e.g. by extending AutoML systems towards optimizing multiple criteria such as fairness or interpretability.

Selected Publication(s):

• F. Pfisterer*, L. Schneider*, J. Moosbauer, M. Binder, and B. Bischl. YAHPO Gym - Design Criteria and a new multifidelity Benchmark for Hyperparameter Optimization, 2022 1st International Conference on Automated Machine Learning.
  LINK
  
• P. Gijsbers*, F. Pfisterer*, J. N. van Rijn, B. Bischl, and J. Vanschoren. Meta-learning for symbolic hyperparameter defaults. In 2021 Genetic and Evolutionary Computa- tion Conference Companion (GECCO ’21 Companion), Lile, France, 2021. ACM
  LINK
  
• F. Pfisterer, J. N. van Rijn, P. Probst, A. Müller, and B. Bischl. Learning multiple defaults for machine learning algorithms. In 2021 Genetic and Evolutionary Computation Conference Companion (GECCO ’21 Companion), Lile, France, 2021. ACM
  LINK
  
• F. Pfisterer, S. Coors, J. Thomas, and B. Bischl. Multi-objective automatic machine learning with AutoxgboostMC. Automating Data Science Workshop at ECML 2019

Algorithmic Fairness

Abstract: My interests in fairness are mostly of practical nature. In my opinion, models that lead to decisions about individuals are already widely deployed (e.g. in credit risk assessment, criminal justice, …) and we need to provide practitioners with the knowledge and tools to assess models with respect to potential biases as well as avenues towards alleviating such problems. I am therefore interested in whether bias can be reliably identified in models and whether bias mitigation strategies reliably reduce biases. In the future I would like to work on the intersection of fairness and AutoML with the hope to provide practitioners with better tooling for bias assessment and mitigation.

Selected Publication(s):

• H. Weerts*, F. Pfisterer*, Matthias Feurer, Katharina Eggensperger, Edward Bergman, Noor Awad, Joaquin Vanschoren, Mykola Pechenizkiy, Bernd Bischl, Frank Hutter. Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML, In Journal of Artificial Intelligence Research 79 (2024): 639-677.
  LINK
  
• A. Agrawal, F. Pfisterer, B. Bischl, J. Chen, S. Sood, S. Shah, F. Buet-Golfouse, B. A. Mateen, and S. Vollmer. Debiasing classifiers: is reality at variance with expectation? 2020.
  LINK
  
• S. Dandl*, F. Pfisterer*, and B. Bischl. Multi-objective counterfactual fairness. In 2022 Genetic and Evolutionary Computation Conference Companion (GECCO ’22 Companion), Lile, France, 2022. ACM
  LINK
  
• F. Pfisterer, S. Wei, S. Vollmer M. Lang and B. Bischl. Fairness Audits And Debiasing Using mlr3fairness
  LINK
  

Benchmarking

Abstract: With the increasing amount of publications proposing new methods, it is often hard to discern methodological advances in the general field from methods that only work in limited, small domains. Benchmarks can provide an unbiased view on this progress and allow readers to select methods that robustly work across several settings. This is especially important for practitioners that lack time and knowledge to implement a variety of methods or to prevent bloating the amount of methods implemented in AutoML tools.

Selected Publication(s):

• F. Pfisterer, C. Harbron, G. Jansen, and T. Xu. Evaluating domain generalization for survival analysis in clinical studies. In G. Flores, G. H. Chen, T. Pollard, J. C. Ho, and T. Naumann, editors, Proceedings of the Conference on Health, Inference, and Learning, volume 174 of Proceedings of Machine Learning Research, pages 32–47. PMLR, 07–08 Apr 2022
  LINK
  
• F. Pfisterer, L. Beggel, X. Sun, F. Scheipl, and B. Bischl. Benchmarking time series classification – functional data vs machine learning approaches, 2019
  LINK
  
• F. Pargent, F. Pfisterer, J. Thomas, and B. Bischl. Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features. Computational Statistics, pages 1–22, 2022
  LINK
  

Software

Abstract: Last but not least, I enjoy developing Open Source software. During my PhD, I have (co-)developed several extensions for the mlr3 ecosystem and contributed to even more.

Selected Software/Publication(s):

• mlr3pipelines: Machine learning pipelines for mlr3.
  M. Binder, F. Pfisterer, M. Lang, L. Schneider, L. Kotthoff, and B. Bischl. mlr3pipelines - flexible machine learning pipelines in R. Journal of Machine Learning Research, 22(184):1–7, 2021
• mcboost: Multi-calibration and multi-accuracy boosting.
  F. Pfisterer, C. Kern, S. Dandl, M. Sun, M. P. Kim, and B. Bischl. mcboost: Multi- calibration boosting for R. Journal of Open Source Software, 6(64):3453, 2021
• mlr3fairness: Bias Audits for mlr3.
  F. Pfisterer, S. Wei, S. Vollmer M. Lang and B. Bischl. Fairness Audits And Debiasing Using mlr3fairness
• mlr3keras: Deep Learning with keras and mlr3.
  A first attempt to integrate keras with mlr3. Superseded by mlr3torch.

* indicates equal contribution