Dr. Giuseppe Casalicchio

About

I finished my PhD at the Working Group Computational Statistics in early 2019 focussing on bechmark experiments, evaluation of machine learning models, and the emerging field of interpretable machine learning. During my PhD, I was also heavily involved in the contribution and development of a variety software projects.

Since March 2019, I am supporting this chair within the framework of the Munich Center for Machine Learning (MCML) as education manager of the Data Science Certificate Program and as PostDoc leading the interpretable machine learning research group. Besides of this, I am part-time CEO of a LMU spin-off education company called Essential Data Science Training GmbH.

Contact

Institut für Statistik

Ludwig-Maximilians-Universität München

Ludwigstraße 33

D-80539 München

Room 344, 3rd floor

Phone: +49 89 2180 3196

giuseppe.casalicchio [at] stat.uni-muenchen.de

Research Interests

You can find me on

Software

References

  1. Herbinger J, Dandl S, Ewald FK, Loibl S, Casalicchio G (2024) Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation. In: In: Nowaczyk S , In: Biecek P , In: Chung NC , In: Vallati M , In: Skruch P , In: Jaworek-Korjakowska J , In: Parkinson S , In: Nikitas A , In: Atzmüller M , In: Kliegr T , In: Schmid U , In: Bobek S , In: Lavrac N , In: Peeters M , In: Dierendonck R van , In: Robben S , In: Mercier-Laurent E , In: Kayakutlu G , In: Owoc ML , In: Mason K , In: Wahid A , In: Bruno P , In: Calimeri F , In: Cauteruccio F , In: Terracina G , In: Wolter D , In: Leidner JL , In: Kohlhase M , In: Dimitrova V (eds) Artificial Intelligence. ECAI 2023 International Workshops, pp. 232–249. Springer Nature Switzerland, Cham.
    link | pdf    .
  2. Dandl S, Casalicchio G, Bischl B, Bothmann L (2023) Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. In: In: Koutra D , In: Plant C , In: Gomez Rodriguez M , In: Baralis E , In: Bonchi F (eds) ECML PKDD 2023: Machine Learning and Knowledge Discovery in Databases: Research Track, pp. 479–495. Springer Nature Switzerland, Cham.
    link | pdf    .
  3. Dandl S, Hofheinz A, Binder M, Bischl B, Casalicchio G (2023) counterfactuals: An R Package for Counterfactual Explanation Methods.
    link | pdf    .
  4. Molnar C, König G, Bischl B, Casalicchio G (2023) Model-agnostic Feature Importance and Effects with Dependent Features–A Conditional Subgroup Approach. Data Mining and Knowledge Discovery.
    link | pdf    .
  5. Scholbeck CA, Funk H, Casalicchio G (2023) Algorithm-Agnostic Feature Attributions for Clustering. In: In: Longo L (ed) Explainable Artificial Intelligence, pp. 217–240. Springer Nature Switzerland, Cham.
    link | pdf    .
  6. Molnar C, Freiesleben T, König G, Herbinger J, Reisinger T, Casalicchio G, Wright MN, Bischl B (2023) Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. In: In: Longo L (ed) Explainable Artificial Intelligence, pp. 456–479. Springer Nature Switzerland, Cham.
    link | pdf    .
  7. Herbinger J, Bischl B, Casalicchio G (2023) Decomposing Global Feature Effects Based on Feature Interactions. arXiv preprint arXiv:2306.00541.
    link | pdf    .
  8. Löwe H, Scholbeck CA, Heumann C, Bischl B, Casalicchio G (2023) fmeffects: An R Package for Forward Marginal Effects. arXiv preprint arXiv:2310.02008.
    link | pdf    .
  9. Scholbeck CA, Moosbauer J, Casalicchio G, Gupta H, Bischl B, Heumann C (2023) Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. arXiv preprint arXiv:2312.13234.
    link | pdf    .
  10. Au Q, Herbinger J, Stachl C, Bischl B, Casalicchio G (2022) Grouped Feature Importance and Combined Features Effect Plot. Data Mining and Knowledge Discovery 36, 1401–1450.
    link | pdf     .
  11. Bothmann L, Strickroth S, Casalicchio G, Rügamer D, Lindauer M, Scheipl F, Bischl B (2022) Developing Open Source Educational Resources for Machine Learning and Data Science. In: In: Kinnaird KM , In: Steinbach P , In: Guhr O (eds) Proceedings of the Third Teaching Machine Learning and Artificial Intelligence Workshop, pp. 1–6. PMLR.
    link | pdf    .
  12. Herbinger J, Bischl B, Casalicchio G (2022) REPID: Regional Effect Plots with implicit Interaction Detection. International Conference on Artificial Intelligence and Statistics (AISTATS) 25.
    link | pdf    .
  13. Moosbauer J, Casalicchio G, Lindauer M, Bischl B (2022) Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. arXiv:2111.14756 [cs.LG].
    link | pdf    .
  14. Nießl C, Herrmann M, Wiedemann C, Casalicchio G, Boulesteix A-L (2022) Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 12, e1441.
    link | pdf    .
  15. Scholbeck CA, Casalicchio G, Molnar C, Bischl B, Heumann C (2022) Marginal Effects for Non-Linear Prediction Functions. To Appear in Data Mining and Knowledge Discovery.
    link | pdf     .
  16. Molnar C, König G, Herbinger J, Freiesleben T, Dandl S, Scholbeck CA, Casalicchio G, Grosse-Wentrup M, Bischl B (2022) General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. In: In: Holzinger A , In: Goebel R , In: Fong R , In: Moon T , In: Müller K-R , In: Samek W (eds) xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers, pp. 39–68. Springer International Publishing, Cham.
    link | pdf    .
  17. Bischl B, Casalicchio G, Feurer M, Gijsbers P, Hutter F, Lang M, Mantovani RG, Rijn JN van, Vanschoren J (2021) OpenML Benchmarking Suites. In: In: Vanschoren J , In: Yeung S (eds) Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks,
    link | pdf    .
  18. Moosbauer J, Herbinger J, Casalicchio G, Lindauer M, Bischl B (2021) Explaining Hyperparameter Optimization via Partial Dependence Plots. Advances in Neural Information Processing Systems (NeurIPS 2021) 34.
    link | pdf    .
  19. Moosbauer J, Herbinger J, Casalicchio G, Lindauer M, Bischl B (2021) Towards Explaining Hyperparameter Optimization via Partial Dependence Plots 8th ICML Workshop on Automated Machine Learning (AutoML),
    link | pdf    .
  20. König G, Freiesleben T, Bischl B, Casalicchio G, Grosse-Wentrup M (2021) Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). arXiv preprint arXiv:2106.08086.
    link | pdf    .
  21. Molnar C, Casalicchio G, Bischl B (2020) Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. In: In: Cellier P , In: Driessens K (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, pp. 193–204. Springer International Publishing, Cham. link | pdf.
  22. Scholbeck CA, Molnar C, Heumann C, Bischl B, Casalicchio G (2020) Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. In: In: Cellier P , In: Driessens K (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2019, pp. 205–216. Springer International Publishing, Cham.
    link | pdf    .
  23. Molnar C, Casalicchio G, Bischl B (2020) Interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges. In: In: Koprinska I , In: Kamp M , In: Appice A , In: Loglisci C , In: Antonie L , In: Zimmermann A , In: Guidotti R , In: Özgöbek Ö , In: Ribeiro RP , In: Gavaldà R , In: Gama J , In: Adilova L , In: Krishnamurthy Y , In: Ferreira PM , In: Malerba D , In: Medeiros I , In: Ceci M , In: Manco G , In: Masciari E , In: Ras ZW , In: Christen P , In: Ntoutsi E , In: Schubert E , In: Zimek A , In: Monreale A , In: Biecek P , In: Rinzivillo S , In: Kille B , In: Lommatzsch A , In: Gulla JA et al. (eds) ECML PKDD 2020 Workshops, pp. 417–431. Springer International Publishing, Cham.
    link | pdf     .
  24. Molnar C, König G, Herbinger J, Freiesleben T, Dandl S, Scholbeck CA, Casalicchio G, Grosse-Wentrup M, Bischl B (2020) Pitfalls to Avoid when Interpreting Machine Learning Models ICML Workshop XXAI: Extending Explainable AI Beyond Deep Models and Classifiers,
    link | pdf    .
  25. Lang M, Binder M, Richter J, Schratz P, Pfisterer F, Coors S, Au Q, Casalicchio G, Kotthoff L, Bischl B (2019) mlr3: A modern object-oriented machine learning framework in R. Journal of Open Source Software 4, 1903.
    link | pdf    .
  26. Au Q, Schalk D, Casalicchio G, Schoedel R, Stachl C, Bischl B (2019) Component-Wise Boosting of Targets for Multi-Output Prediction. arXiv preprint arXiv:1904.03943.
    link | pdf    .
  27. Casalicchio G, Molnar C, Bischl B (2019) Visualizing the Feature Importance for Black Box Models. In: In: Berlingerio M , In: Bonchi F , In: Gärtner T , In: Hurley N , In: Ifrim G (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2018, pp. 655–670. Springer International Publishing, Cham.
    link | pdf    .
  28. Molnar C, Casalicchio G, Bischl B (2018) iml: An R package for Interpretable Machine Learning. The Journal of Open Source Software 3, 786.
    link | pdf    .
  29. Casalicchio G, Lesaffre E, Küchenhoff H, Bruyneel L (2017) Nonlinear Analysis to Detect if Excellent Nursing Work Environments Have Highest Well-Being. Journal of Nursing Scholarship 49, 537–547.
    link | pdf    .
  30. Casalicchio G, Bossek J, Lang M, Kirchhoff D, Kerschke P, Hofner B, Seibold H, Vanschoren J, Bischl B (2017) OpenML: An R package to connect to the machine learning platform OpenML. Computational Statistics, 977–991.
    link | pdf    .
  31. Probst P, Au Q, Casalicchio G, Stachl C, Bischl B (2017) Multilabel Classification with R Package mlr. The R Journal 9, 352–369.
    link | pdf    .
  32. Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, Casalicchio G, Jones ZM (2016) mlr: Machine Learning in R. The Journal of Machine Learning Research 17, 5938–5942.
    link | pdf    .
  33. Casalicchio G, Bischl B, Boulesteix A-L, Schmid M (2015) The residual-based predictiveness curve: A visual tool to assess the performance of prediction models. Biometrics 72, 392–401.
    link | pdf    .
  34. Casalicchio G, Tutz G, Schauberger G (2015) Subject-specific Bradley–Terry–Luce models with implicit variable selection. Statistical Modelling 15, 526–547.
    link | pdf    .
  35. Vanschoren J, Rijn JN van, Bischl B, Casalicchio G, Feurer M (2015) OpenML: A Networked Science Platform for Machine Learning 2015 ICML Workshop on Machine Learning Open Source Software (MLOSS 2015), pp. 1–3.
    link | pdf    .
  36. Bergmann S, Ziegler N, Bartels T, Hübel J, Schumacher C, Rauch E, Brandl S, Bender A, Casalicchio G, Krautwald-Junghanns M-E, others (2013) Prevalence and severity of foot pad alterations in German turkey poults during the early rearing phase. Poultry science 92, 1171–1176.
    link | pdf    .
  37. Ziegler N, Bergmann S, Huebei J, Bartels T, Schumacher C, Bender A, Casalicchio G, Kuechenhoff H, Krautwald-Junghanns M-E, Erhard M (2013) Climate parameters and the influence on the foot pad health status of fattening turkeys BUT 6 during the early rearing phase. BERLINER UND MUNCHENER TIERARZTLICHE WOCHENSCHRIFT 126, 181–188.
    link | pdf    .