Software
compboost
R Framework for component-wise boosting.
mlr3tuning
Tuning support for mlr3.
dsBinVal
Distributed ROC analysis with DataSHIELD.
Talks and Posts
24. March 2023
Disputation of my of my dissertation "modern approaches for component-wise boosting".
18. December 2022
Talk about making component-wise boosting more efficient.
12. July 2022
Talk about model transportability and privacy protection at the 31st International Biometric Conference.
14. March 2021
Talk about distributed calculation of the AUC at the 67th Biometric Colloquium.
12. July 2019
UseR! talk about compboost.
29. November 2018
Talk about compboost at the Applied R Meetup.
10. July 2018
Talk about compboost at Statistical Computing 2018 at Reisensburg Castle.
3. July 2018
Beginner mlr workshop at the whyR conference in Wroclaw.
19. December 2017
Rcpp Gallery article about custom printer methods for exposed C++ classes.
13. December 2017
Article about our win of the Munich Re Datathon.
18. October 2017
Blog post about our win of the TEF Data Challenge hosted by Telefonica.
Publications
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Schalk, D., Hoffmann, V. S., Bischl, B., & Mansmann, U. (2023). dsBinVal: Conducting distributed ROC analysis using DataSHIELD. Journal of Open Source Software, 8(82), 4545, DOI: 10.21105/joss.04545.
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Akkus C., Chu L., Djakovic V., Jauch-Walser S., Koch P., Loss G., Marquardt C., Moldovan M., Sauter N., Schneider M., Schulte R., Urbanczyk K., Goschenhofer J., Heumann C., Rasmus Hvingelby R., Schalk D., Aßenmacher M. (2023) Multimodal Deep Learning. arXiv preprint, DOI: 10.48550/arXiv.2301.04856.
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Schalk D., Bischl B., Rügamer D. (2022) Privacy-preserving and lossless distributed estimation of high-dimensional generalized additive mixed models. Currently under review in the Journal of Computational and Graphical Statistics.
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Schalk D., Bischl B., Rügamer D. (2022) Accelerated Componentwise Gradient Boosting using Efficient Data Representation and Momentum-based Optimization. Journal of Computational and Graphical Statistics, DOI: 10.1080/10618600.2022.2116446.
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Schalk D., Hoffmann V. S., Bischl B., Mansmann U. (2022) Distributed non-disclosive validation of predictive models by a modified ROC-GLM. arXiv preprint, DOI: 10.48550/arXiv.2203.10828.
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*Coors S., *Schalk D., Bischl B., Rügamer D. (2021) Automatic Componentwise Boosting: An Interpretable AutoML System. ECML-PKDD Workshop on Automating Data Science.
*Shared first authorship
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Au Q., Schalk D., Casalicchio G., Schoedel R., Stachl C., Bischl B. (2019) Component-Wise Boosting of Targets for Multi-Output Prediction. arXiv preprint, DOI: 10.48550/arXiv.1904.03943.
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Schalk D., Thomas J., Bischl B. (2018) compboost: Modular Framework for Component-Wise Boosting. Journal of Open Source Software, 3(30), 967, DOI: 10.21105/joss.00967.