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Giuseppe Casalicchio
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2020 – today
- 2024
- [j10]Christoph Molnar
, Gunnar König
Model-agnostic feature importance and effects with dependent features: a conditional subgroup approach. Data Min. Knowl. Discov. 38(5): 2903-2941 (2024) - [j9]Christian A. Scholbeck
Marginal effects for non-linear prediction functions. Data Min. Knowl. Discov. 38(5): 2997-3042 (2024) - [j8]Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann:
Correction: Marginal effects for non-linear prediction functions. Data Min. Knowl. Discov. 38(6): 4234-4235 (2024) - [c17]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. ICML 2024 - [c16]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
On the Robustness of Global Feature Effect Explanations. ECML/PKDD (2) 2024: 125-142 - [c15]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 - [c14]Fiona Katharina Ewald, Ludwig Bothmann
A Guide to Feature Importance Methods for Scientific Inference. xAI (2) 2024: 440-464 - [i31]Julian Rodemann, Federico Croppi, Philipp Arens, Yusuf Sale, Julia Herbinger, Bernd Bischl, Eyke Hüllermeier, Thomas Augustin, Conor J. Walsh, Giuseppe Casalicchio:
Explaining Bayesian Optimization by Shapley Values Facilitates Human-AI Collaboration. CoRR abs/2403.04629 (2024) - [i30]Vasilis Gkolemis, Christos Diou, Eirini Ntoutsi, Theodore Dalamagas, Bernd Bischl, Julia Herbinger, Giuseppe Casalicchio:
Effector: A Python package for regional explanations. CoRR abs/2404.02629 (2024) - [i29]Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König:
A Guide to Feature Importance Methods for Scientific Inference. CoRR abs/2404.12862 (2024) - [i28]Susanne Dandl, Marc Becker, Bernd Bischl, Giuseppe Casalicchio, Ludwig Bothmann:
mlr3summary: Concise and interpretable summaries for machine learning models. CoRR abs/2404.16899 (2024) - [i27]Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl:
Position: Why We Must Rethink Empirical Research in Machine Learning. CoRR abs/2405.02200 (2024) - [i26]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
On the Robustness of Global Feature Effect Explanations. CoRR abs/2406.09069 (2024) - [i25]Hubert Baniecki, Giuseppe Casalicchio, Bernd Bischl, Przemyslaw Biecek:
Efficient and Accurate Explanation Estimation with Distribution Compression. CoRR abs/2406.18334 (2024) - 2023
- [c13]Julia Herbinger
Leveraging Model-Based Trees as Interpretable Surrogate Models for Model Distillation. ECAI Workshops (1) 2023: 232-249 - [c12]Susanne Dandl
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. ECML/PKDD (3) 2023: 479-495 - [c11]Christian A. Scholbeck
Algorithm-Agnostic Feature Attributions for Clustering. xAI (1) 2023: 217-240 - [c10]Christoph Molnar
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. xAI (1) 2023: 456-479 - [i24]Susanne Dandl, Andreas Hofheinz, Martin Binder, Bernd Bischl, Giuseppe Casalicchio:
counterfactuals: An R Package for Counterfactual Explanation Methods. CoRR abs/2304.06569 (2023) - [i23]Susanne Dandl, Giuseppe Casalicchio, Bernd Bischl, Ludwig Bothmann:
Interpretable Regional Descriptors: Hyperbox-Based Local Explanations. CoRR abs/2305.02780 (2023) - [i22]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
Decomposing Global Feature Effects Based on Feature Interactions. CoRR abs/2306.00541 (2023) - [i21]Holger Löwe, Christian A. Scholbeck, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio:
fmeffects: An R Package for Forward Marginal Effects. CoRR abs/2310.02008 (2023) - [i20]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) - [i19]Christian A. Scholbeck, Julia Moosbauer, Giuseppe Casalicchio, Hoshin Gupta, Bernd Bischl, Christian Heumann:
Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis. CoRR abs/2312.13234 (2023) - 2022
- [j7]Quay Au, Julia Herbinger
Grouped feature importance and combined features effect plot. Data Min. Knowl. Discov. 36(4): 1401-1450 (2022) - [j6]Christina Nießl
Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results. WIREs Data Mining Knowl. Discov. 12(2) (2022) - [c9]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
REPID: Regional Effect Plots with implicit Interaction Detection. AISTATS 2022: 10209-10233 - [c8]Ludwig Bothmann, Sven Strickroth
Developing Open Source Educational Resources for Machine Learning and Data Science. Teaching ML 2022: 1-6 - [i18]Christian A. Scholbeck, Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl, Christian Heumann:
Marginal Effects for Non-Linear Prediction Functions. CoRR abs/2201.08837 (2022) - [i17]Julia Herbinger, Bernd Bischl, Giuseppe Casalicchio:
REPID: Regional Effect Plots with implicit Interaction Detection. CoRR abs/2202.07254 (2022) - [i16]Julia Moosbauer, Giuseppe Casalicchio
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution. CoRR abs/2206.05447 (2022) - [i15]Christian A. Scholbeck, Henri Funk, Giuseppe Casalicchio:
Algorithm-Agnostic Interpretations for Clustering. CoRR abs/2209.10578 (2022) - 2021
- [c7]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Pieter Gijsbers, Frank Hutter, Michel Lang
OpenML Benchmarking Suites. NeurIPS Datasets and Benchmarks 2021 - [c6]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. NeurIPS 2021: 2280-2291 - [i14]Quay Au, Julia Herbinger, Clemens Stachl, Bernd Bischl, Giuseppe Casalicchio:
Grouped Feature Importance and Combined Features Effect Plot. CoRR abs/2104.11688 (2021) - [i13]Gunnar König, Timo Freiesleben, Bernd Bischl, Giuseppe Casalicchio, Moritz Grosse-Wentrup:
Decomposition of Global Feature Importance into Direct and Associative Components (DEDACT). CoRR abs/2106.08086 (2021) - [i12]Ludwig Bothmann, Sven Strickroth
Developing Open Source Educational Resources for Machine Learning and Data Science. CoRR abs/2107.14330 (2021) - [i11]Christoph Molnar, Timo Freiesleben, Gunnar König, Giuseppe Casalicchio, Marvin N. Wright, Bernd Bischl:
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process. CoRR abs/2109.01433 (2021) - [i10]Julia Moosbauer, Julia Herbinger, Giuseppe Casalicchio, Marius Lindauer, Bernd Bischl:
Explaining Hyperparameter Optimization via Partial Dependence Plots. CoRR abs/2111.04820 (2021) - 2020
- [c5]Christoph Molnar
General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models. xxAI@ICML 2020: 39-68 - [c4]Christoph Molnar
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. PKDD/ECML Workshops 2020: 417-431 - [i9]Christoph Molnar, Gunnar König, Bernd Bischl, Giuseppe Casalicchio:
Model-agnostic Feature Importance and Effects with Dependent Features - A Conditional Subgroup Approach. CoRR abs/2006.04628 (2020) - [i8]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) - [i7]Christoph Molnar, Giuseppe Casalicchio, Bernd Bischl:
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges. CoRR abs/2010.09337 (2020)
2010 – 2019
- 2019
- [b1]Giuseppe Casalicchio:
On benchmark experiments and visualization methods for the evaluation and interpretation of machine learning models. Ludwig Maximilian University of Munich, Germany, 2019 - [j5]Giuseppe Casalicchio
OpenML: An R package to connect to the machine learning platform OpenML. Comput. Stat. 34(3): 977-991 (2019) - [j4]Michel Lang
mlr3: A modern object-oriented machine learning framework in R. J. Open Source Softw. 4(44): 1903 (2019) - [c3]Christoph Molnar, Giuseppe Casalicchio
Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability. PKDD/ECML Workshops (1) 2019: 193-204 - [c2]Christian A. Scholbeck, Christoph Molnar, Christian Heumann, Bernd Bischl, Giuseppe Casalicchio
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model-Agnostic Interpretations. PKDD/ECML Workshops (1) 2019: 205-216 - [d1]Michel Lang
mlr3: A modern object-oriented machine learning framework in R. Zenodo, 2019 - [i6]Christoph Molnar
Quantifying Interpretability of Arbitrary Machine Learning Models Through Functional Decomposition. CoRR abs/1904.03867 (2019) - [i5]Quay Au, Daniel Schalk, Giuseppe Casalicchio, Ramona Schödel, Clemens Stachl, Bernd Bischl:
Component-Wise Boosting of Targets for Multi-Output Prediction. CoRR abs/1904.03943 (2019) - [i4]Christian A. Scholbeck, Christoph Molnar
Sampling, Intervention, Prediction, Aggregation: A Generalized Framework for Model Agnostic Interpretations. CoRR abs/1904.03959 (2019) - 2018
- [j3]Christoph Molnar
iml: An R package for Interpretable Machine Learning. J. Open Source Softw. 3(26): 786 (2018) - [c1]Giuseppe Casalicchio
Visualizing the Feature Importance for Black Box Models. ECML/PKDD (1) 2018: 655-670 - [i3]Giuseppe Casalicchio, Christoph Molnar, Bernd Bischl:
Visualizing the Feature Importance for Black Box Models. CoRR abs/1804.06620 (2018) - 2017
- [j2]Philipp Probst, Quay Au, Giuseppe Casalicchio, Clemens Stachl, Bernd Bischl:
Multilabel Classification with R Package mlr. R J. 9(1): 352 (2017) - [i2]Giuseppe Casalicchio, Jakob Bossek, Michel Lang
OpenML: An R Package to Connect to the Networked Machine Learning Platform OpenML. CoRR abs/1701.01293 (2017) - [i1]Bernd Bischl, Giuseppe Casalicchio, Matthias Feurer, Frank Hutter, Michel Lang
OpenML Benchmarking Suites and the OpenML100. CoRR abs/1708.03731 (2017) - 2016
- [j1]Bernd Bischl, Michel Lang, Lars Kotthoff, Julia Schiffner, Jakob Richter, Erich Studerus, Giuseppe Casalicchio, Zachary M. Jones:
mlr: Machine Learning in R. J. Mach. Learn. Res. 17: 170:1-170:5 (2016)
Coauthor Index
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last updated on 2024-11-08 20:33 CET by the dblp team
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