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ORCIDSusanne Dandl
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2020 – today
- 2024
[c8]Susanne Dandl
, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright:
CountARFactuals - Generating Plausible Model-Agnostic Counterfactual Explanations with Adversarial Random Forests. xAI (3) 2024: 85-107- [c7]Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann:
mlr3summary: Concise and interpretable summaries for machine learning models. xAI (Late-breaking Work, Demos, Doctoral Consortium) 2024: 281-288 - [i8]Susanne Dandl, Kristin Blesch, Timo Freiesleben, Gunnar König, Jan Kapar, Bernd Bischl, Marvin N. Wright:
CountARFactuals - Generating plausible model-agnostic counterfactual explanations with adversarial random forests. CoRR abs/2404.03506 (2024) - [i7]Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann:
mlr3summary: Concise and interpretable summaries for machine learning models. CoRR abs/2404.16899 (2024) - 2023
- [b1]Susanne Dandl:
Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations. Ludwig Maximilian University of Munich, Germany, 2023 - [c6]Ludwig Bothmann, Susanne Dandl, Michael Schomaker:
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions. AEQUITAS@ECAI 2023 - [c5]Julia Herbinger, Susanne Dandl, Fiona Katharina Ewald, Sofia Loibl, Giuseppe Casalicchio:
Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation. ECAI Workshops (1) 2023: 232-249 - [c4]Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. ECML/PKDD (3) 2023: 479-495 - [i6]Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio:
counterfactuals: An R Package for Counterfactual Explanation Methods. CoRR abs/2304.06569 (2023) - [i5]Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. CoRR abs/2305.02780 (2023) - [i4]Ludwig Bothmann, Susanne Dandl, Michael Schomaker:
Causal Fair Machine Learning via Rank-Preserving Interventional Distributions. CoRR abs/2307.12797 (2023) - [i3]Julia Herbinger, Susanne Dandl, Fiona Katharina Ewald, Sofia Loibl, Giuseppe Casalicchio:
Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillation. CoRR abs/2310.03112 (2023) - 2022
- [c3]Susanne Dandl, Florian Pfisterer, Bernd Bischl:
Multi-objective counterfactual fairness. GECCO Companion 2022: 328-331 - 2021
- [j1]Florian Pfisterer, Christoph Kern, Susanne Dandl, Matthew Sun, Michael Kim, Bernd Bischl:
mcboost: Multi-Calibration Boosting for R. J. Open Source Softw. 6(64): 3453 (2021) - [d1]Florian Pfisterer, Christoph Kern, Susanne Dandl, Mathew Sun, Michael P. Kim, Bernd Bischl:
mcboost: Multi-Calibration Boosting for R. Zenodo, 2021
- 2020
- [c2]Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. xxAI@ICML 2020: 39-68 - [c1]Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl:
Multi-Objective Counterfactual Explanations. PPSN (1) 2020: 448-469 - [i2]Susanne Dandl, Christoph Molnar, Martin Binder, Bernd Bischl:
Multi-Objective Counterfactual Explanations. CoRR abs/2004.11165 (2020) - [i1]Christoph Molnar, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup, Bernd Bischl:
Pitfalls to Avoid when Interpreting Machine Learning Models. CoRR abs/2007.04131 (2020)
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