Rethinking Explainable Machine Learning as Applied Statistics
Fuente:
arXiv
Saved in:
| Main Authors: | Bordt, Sebastian, Raidl, Eric, von Luxburg, Ulrike |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling
by: Haas, Moritz, et al.
Published: (2025)
by: Haas, Moritz, et al.
Published: (2025)
Informative Post-Hoc Explanations Only Exist for Simple Functions
by: Günther, Eric, et al.
Published: (2025)
by: Günther, Eric, et al.
Published: (2025)
How Much Can We Forget about Data Contamination?
by: Bordt, Sebastian, et al.
Published: (2024)
by: Bordt, Sebastian, et al.
Published: (2024)
The Manifold Hypothesis for Gradient-Based Explanations
by: Bordt, Sebastian, et al.
Published: (2022)
by: Bordt, Sebastian, et al.
Published: (2022)
Using predictive multiplicity to measure individual performance within the AI Act
by: Frohnapfel, Karolin, et al.
Published: (2026)
by: Frohnapfel, Karolin, et al.
Published: (2026)
Disentangling Interactions and Dependencies in Feature Attribution
by: König, Gunnar, et al.
Published: (2024)
by: König, Gunnar, et al.
Published: (2024)
Auditing Local Explanations is Hard
by: Bhattacharjee, Robi, et al.
Published: (2024)
by: Bhattacharjee, Robi, et al.
Published: (2024)
How to safely discard features based on aggregate SHAP values
by: Bhattacharjee, Robi, et al.
Published: (2025)
by: Bhattacharjee, Robi, et al.
Published: (2025)
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension
by: Haas, Moritz, et al.
Published: (2023)
by: Haas, Moritz, et al.
Published: (2023)
Train Once, Answer All: Many Pretraining Experiments for the Cost of One
by: Bordt, Sebastian, et al.
Published: (2025)
by: Bordt, Sebastian, et al.
Published: (2025)
Elephants Never Forget: Testing Language Models for Memorization of Tabular Data
by: Bordt, Sebastian, et al.
Published: (2024)
by: Bordt, Sebastian, et al.
Published: (2024)
Performative Validity of Recourse Explanations
by: König, Gunnar, et al.
Published: (2025)
by: König, Gunnar, et al.
Published: (2025)
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
by: Srinivas, Suraj, et al.
Published: (2023)
by: Srinivas, Suraj, et al.
Published: (2023)
Statistical Inference for Explainable Boosting Machines
by: Fang, Haimo, et al.
Published: (2026)
by: Fang, Haimo, et al.
Published: (2026)
Clustering with Tangles: Algorithmic Framework and Theoretical Guarantees
by: Klepper, Solveig, et al.
Published: (2020)
by: Klepper, Solveig, et al.
Published: (2020)
Data Science with LLMs and Interpretable Models
by: Bordt, Sebastian, et al.
Published: (2024)
by: Bordt, Sebastian, et al.
Published: (2024)
Elephants Never Forget: Memorization and Learning of Tabular Data in Large Language Models
by: Bordt, Sebastian, et al.
Published: (2024)
by: Bordt, Sebastian, et al.
Published: (2024)
How to Scale Mixture-of-Experts: From muP to the Maximally Scale-Stable Parameterization
by: Vankadara, Leena Chennuru, et al.
Published: (2026)
by: Vankadara, Leena Chennuru, et al.
Published: (2026)
Rethinking Distance Metrics for Counterfactual Explainability
by: Williams, Joshua Nathaniel, et al.
Published: (2024)
by: Williams, Joshua Nathaniel, et al.
Published: (2024)
Outlier Detection in Plantar Pressure: Human-Centered Comparison of Statistical Parametric Mapping and Explainable Machine Learning
by: Dindorf, Carlo, et al.
Published: (2025)
by: Dindorf, Carlo, et al.
Published: (2025)
An Explainable Pipeline for Machine Learning with Functional Data
by: Goode, Katherine, et al.
Published: (2025)
by: Goode, Katherine, et al.
Published: (2025)
Rethinking Explainability in the Era of Multimodal AI
by: Agarwal, Chirag
Published: (2025)
by: Agarwal, Chirag
Published: (2025)
Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
by: Freiesleben, Timo, et al.
Published: (2026)
by: Freiesleben, Timo, et al.
Published: (2026)
Efficient Milling Quality Prediction with Explainable Machine Learning
by: Gross, Dennis, et al.
Published: (2024)
by: Gross, Dennis, et al.
Published: (2024)
Utilizing Large Language Models for Machine Learning Explainability
by: Vassiliades, Alexandros, et al.
Published: (2025)
by: Vassiliades, Alexandros, et al.
Published: (2025)
Applying Machine Learning Tools for Urban Resilience Against Floods
by: Pour, Mahla Ardebili, et al.
Published: (2024)
by: Pour, Mahla Ardebili, et al.
Published: (2024)
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling
by: Xiao, Lechao
Published: (2024)
by: Xiao, Lechao
Published: (2024)
Rethinking Robustness in Machine Learning: A Posterior Agreement Approach
by: Carvalho, João Borges S., et al.
Published: (2025)
by: Carvalho, João Borges S., et al.
Published: (2025)
Rethinking and Recomputing the Value of Machine Learning Models
by: Sayin, Burcu, et al.
Published: (2022)
by: Sayin, Burcu, et al.
Published: (2022)
An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction
by: Rifat, Md. Asif Khan, et al.
Published: (2024)
by: Rifat, Md. Asif Khan, et al.
Published: (2024)
Explainable Machine-Learning based Detection of Knee Injuries in Runners
by: Fuentes-Jiménez, David, et al.
Published: (2026)
by: Fuentes-Jiménez, David, et al.
Published: (2026)
Rethinking Deep Learning: Propagating Information in Neural Networks without Backpropagation and Statistical Optimization
by: Itoh, Kei
Published: (2024)
by: Itoh, Kei
Published: (2024)
Feature Importance and Explainability in Quantum Machine Learning
by: Power, Luke, et al.
Published: (2024)
by: Power, Luke, et al.
Published: (2024)
On the Relationship Between Interpretability and Explainability in Machine Learning
by: Leblanc, Benjamin, et al.
Published: (2023)
by: Leblanc, Benjamin, et al.
Published: (2023)
Investigating the Duality of Interpretability and Explainability in Machine Learning
by: Garouani, Moncef, et al.
Published: (2025)
by: Garouani, Moncef, et al.
Published: (2025)
Explainable Machine Learning for ICU Readmission Prediction
by: de Sá, Alex G. C., et al.
Published: (2023)
by: de Sá, Alex G. C., et al.
Published: (2023)
Position: Why We Must Rethink Empirical Research in Machine Learning
by: Herrmann, Moritz, et al.
Published: (2024)
by: Herrmann, Moritz, et al.
Published: (2024)
Explainable Graph-theoretical Machine Learning: with Application to Alzheimer's Disease Prediction
by: Baghirova, Narmina, et al.
Published: (2025)
by: Baghirova, Narmina, et al.
Published: (2025)
Evaluating Local Explainability Metrics for Machine Learning Models on Tabular Data
by: Pereira, Tomás, et al.
Published: (2026)
by: Pereira, Tomás, et al.
Published: (2026)
Data Model Design for Explainable Machine Learning-based Electricity Applications
by: Fortuna, Carolina, et al.
Published: (2025)
by: Fortuna, Carolina, et al.
Published: (2025)
Similar Items
-
On the Surprising Effectiveness of Large Learning Rates under Standard Width Scaling
by: Haas, Moritz, et al.
Published: (2025) -
Informative Post-Hoc Explanations Only Exist for Simple Functions
by: Günther, Eric, et al.
Published: (2025) -
How Much Can We Forget about Data Contamination?
by: Bordt, Sebastian, et al.
Published: (2024) -
The Manifold Hypothesis for Gradient-Based Explanations
by: Bordt, Sebastian, et al.
Published: (2022) -
Using predictive multiplicity to measure individual performance within the AI Act
by: Frohnapfel, Karolin, et al.
Published: (2026)