Image de couverture de livre

Achieving interpretable machine learning by functional decomposition of black-box models into explainable predictor effects

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Köhler, David, Rügamer, David, Schmid, Matthias
Format:	Preprint
Publié:	2024
Sujets:	Machine Learning
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Scalable Higher-Order Tensor Product Spline Models
par: Rügamer, David
Publié: (2024)

Hoeffding decomposition of black-box models with dependent inputs
par: Idrissi, Marouane Il, et autres
Publié: (2023)

On the Epistemic Uncertainty of Overparametrized Neural Networks
par: Rügamer, David
Publié: (2026)

Surrogate modeling for interpreting black-box LLMs in medical predictions
par: Han, Changho, et autres
Publié: (2026)

Rethinking Intrinsic Dimension Estimation in Neural Representations
par: Schulte, Rickmer, et autres
Publié: (2026)

Additive Model Boosting: New Insights and Path(ologie)s
par: Schulte, Rickmer, et autres
Publié: (2025)

Position: The Time for Sampling Is Now! Charting a New Course for Bayesian Deep Learning
par: Sommer, Emanuel, et autres
Publié: (2026)

survex: an R package for explaining machine learning survival models
par: Spytek, Mikołaj, et autres
Publié: (2023)

Opening the AI black box: program synthesis via mechanistic interpretability
par: Michaud, Eric J., et autres
Publié: (2024)

Soft-CAM: Making black box models self-explainable for medical image analysis
par: Djoumessi, Kerol, et autres
Publié: (2025)

HFNO: an interpretable data-driven decomposition strategy for turbulent flows
par: Cayuela, Marco, et autres
Publié: (2025)

mlr3summary: Concise and interpretable summaries for machine learning models
par: Dandl, Susanne, et autres
Publié: (2024)

Opportunities and limitations of explaining quantum machine learning
par: Gil-Fuster, Elies, et autres
Publié: (2024)

REX: Causal discovery based on machine learning and explainability techniques
par: Renero, Jesus, et autres
Publié: (2025)

Are machine learning interpretations reliable? A stability study on global interpretations
par: Gan, Luqin, et autres
Publié: (2025)

Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importance
par: Kozielski, Michał, et autres
Publié: (2024)

Optimization on black-box function by parameter-shift rule
par: Hai, Vu Tuan
Publié: (2025)

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors
par: Nagler, Thomas, et autres
Publié: (2025)

Examining spatial heterogeneity of ridesourcing demand determinants with explainable machine learning
par: Zhang, Xiaojian, et autres
Publié: (2022)

Enhancing Heavy Rain Nowcasting with Multimodal Data: Integrating Radar and Satellite Observations
par: Kassoumeh, Rama, et autres
Publié: (2025)

Predicting life satisfaction using machine learning and explainable AI
par: Khan, Alif Elham, et autres
Publié: (2025)

Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning
par: Bastawrous, Mary V., et autres
Publié: (2024)

Accurate predictive model of band gap with selected important features based on explainable machine learning
par: Lee, Joohwi, et autres
Publié: (2025)

META-ANOVA: Screening interactions for interpretable machine learning
par: Choi, Yongchan, et autres
Publié: (2024)

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
par: Beaglehole, Daniel, et autres
Publié: (2025)

Pilot selection in the era of Virtual reality: algorithms for accurate and interpretable machine learning models
par: Ke, Luoma, et autres
Publié: (2025)

An efficient, accurate, and interpretable machine learning method for computing probability of failure
par: Zhu, Jacob, et autres
Publié: (2026)

Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses
par: Marandi, Ramtin Zargari, et autres
Publié: (2025)

A hierarchical decomposition for explaining ML performance discrepancies
par: Feng, Jean, et autres
Publié: (2024)

A general learning scheme for classical and quantum Ising machines
par: Schmid, Ludwig, et autres
Publié: (2023)

Stabilizing black-box model selection with the inflated argmax
par: Adrian, Melissa, et autres
Publié: (2024)

An explainable machine learning approach for energy forecasting at the household level
par: Béraud, Pauline, et autres
Publié: (2024)

Can machine learning for quantum-gas experiments be explainable?
par: Zwolak, I. B. Spielman amd J. P.
Publié: (2026)

Explanatory machine learning for sequential human teaching
par: Ai, Lun, et autres
Publié: (2022)

Stage-specific cancer survival prediction enriched by explainable machine learning
par: Poorhasani, Parisa, et autres
Publié: (2026)

Recent advances in interpretable machine learning using structure-based protein representations
par: Vecchietti, Luiz Felipe, et autres
Publié: (2024)

A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
par: Chowdhury, Prithwijit, et autres
Publié: (2026)

Domain Bridge: Generative model-based domain forensic for black-box models
par: Zhang, Jiyi, et autres
Publié: (2024)

Bayesian Semi-structured Subspace Inference
par: Dold, Daniel, et autres
Publié: (2024)

bde: A Python Package for Bayesian Deep Ensembles via MILE
par: Arvanitis, Vyron, et autres
Publié: (2026)