Interpretable Machine Learning for Survival Analysis
Fuente:
arXiv
Saved in:
| Main Authors: | Langbein, Sophie Hanna, Krzyziński, Mateusz, Spytek, Mikołaj, Baniecki, Hubert, Biecek, Przemysław, Wright, Marvin N. |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Interpretable Machine Learning for Survival Analysis
by: Sophie Hanna Langbein, et al.
Published: (2025)
by: Sophie Hanna Langbein, et al.
Published: (2025)
survex: an R package for explaining machine learning survival models
by: Spytek, Mikołaj, et al.
Published: (2023)
by: Spytek, Mikołaj, et al.
Published: (2023)
Performance is not enough: the story told by a Rashomon quartet
by: Biecek, Przemyslaw, et al.
Published: (2023)
by: Biecek, Przemyslaw, et al.
Published: (2023)
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2026)
by: Langbein, Sophie Hanna, et al.
Published: (2026)
Attributions All the Way Down? The Metagame of Interpretability
by: Baniecki, Hubert, et al.
Published: (2026)
by: Baniecki, Hubert, et al.
Published: (2026)
Birds look like cars: Adversarial analysis of intrinsically interpretable deep learning
by: Baniecki, Hubert, et al.
Published: (2025)
by: Baniecki, Hubert, et al.
Published: (2025)
Imputation Uncertainty in Interpretable Machine Learning Methods
by: Golchian, Pegah, et al.
Published: (2025)
by: Golchian, Pegah, et al.
Published: (2025)
Interpreting CLIP with Hierarchical Sparse Autoencoders
by: Zaigrajew, Vladimir, et al.
Published: (2025)
by: Zaigrajew, Vladimir, et al.
Published: (2025)
Adversarial attacks and defenses in explainable artificial intelligence: A survey
by: Baniecki, Hubert, et al.
Published: (2023)
by: Baniecki, Hubert, et al.
Published: (2023)
Gradient-based Explanations for Deep Learning Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2025)
by: Langbein, Sophie Hanna, et al.
Published: (2025)
The Grammar of Interactive Explanatory Model Analysis
by: Baniecki, Hubert, et al.
Published: (2020)
by: Baniecki, Hubert, et al.
Published: (2020)
On the Robustness of Global Feature Effect Explanations
by: Baniecki, Hubert, et al.
Published: (2024)
by: Baniecki, Hubert, et al.
Published: (2024)
Efficient and Accurate Explanation Estimation with Distribution Compression
by: Baniecki, Hubert, et al.
Published: (2024)
by: Baniecki, Hubert, et al.
Published: (2024)
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data
by: Kobylińska, Katarzyna, et al.
Published: (2023)
by: Kobylińska, Katarzyna, et al.
Published: (2023)
SwordBench: Evaluating Orthogonality of Steering Image Representations
by: Zaigrajew, Vladimir, et al.
Published: (2026)
by: Zaigrajew, Vladimir, et al.
Published: (2026)
Machine Learning in Epidemiology
by: Wright, Marvin N., et al.
Published: (2026)
by: Wright, Marvin N., et al.
Published: (2026)
What if? Causal Machine Learning in Supply Chain Risk Management
by: Wyrembek, Mateusz, et al.
Published: (2024)
by: Wyrembek, Mateusz, et al.
Published: (2024)
Red-Teaming Segment Anything Model
by: Jankowski, Krzysztof, et al.
Published: (2024)
by: Jankowski, Krzysztof, et al.
Published: (2024)
Horseshoe Forests for High-Dimensional Causal Survival Analysis
by: Jacobs, Tijn, et al.
Published: (2025)
by: Jacobs, Tijn, et al.
Published: (2025)
Explaining Similarity in Vision-Language Encoders with Weighted Banzhaf Interactions
by: Baniecki, Hubert, et al.
Published: (2025)
by: Baniecki, Hubert, et al.
Published: (2025)
Amortized Causal Discovery with Prior-Fitted Networks
by: Sypniewski, Mateusz, et al.
Published: (2025)
by: Sypniewski, Mateusz, et al.
Published: (2025)
Interpretable machine learning for time-to-event prediction in medicine and healthcare
by: Baniecki, Hubert, et al.
Published: (2023)
by: Baniecki, Hubert, et al.
Published: (2023)
Doubly Robust Conformalized Survival Analysis with Right-Censored Data
by: Sesia, Matteo, et al.
Published: (2024)
by: Sesia, Matteo, et al.
Published: (2024)
Casewise and Cellwise Robust Multilinear Principal Component Analysis
by: Hirari, Mehdi, et al.
Published: (2025)
by: Hirari, Mehdi, et al.
Published: (2025)
Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
by: Ham, Dae Woong, et al.
Published: (2022)
by: Ham, Dae Woong, et al.
Published: (2022)
Interpretable Deep Learning Methods for Multiview Learning
by: Wang, Hengkang, et al.
Published: (2023)
by: Wang, Hengkang, et al.
Published: (2023)
Position: Stop Chasing the C-index when Evaluating Survival Analysis Models
by: Lillelund, Christian Marius, et al.
Published: (2025)
by: Lillelund, Christian Marius, et al.
Published: (2025)
A Guide to Feature Importance Methods for Scientific Inference
by: Ewald, Fiona Katharina, et al.
Published: (2024)
by: Ewald, Fiona Katharina, et al.
Published: (2024)
HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks
by: Liu, Xin, et al.
Published: (2024)
by: Liu, Xin, et al.
Published: (2024)
Enhancing Airline Customer Satisfaction: A Machine Learning and Causal Analysis Approach
by: Mirthipati, Tejas
Published: (2024)
by: Mirthipati, Tejas
Published: (2024)
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
by: Blocher, Hannah, et al.
Published: (2023)
by: Blocher, Hannah, et al.
Published: (2023)
Combining SHAP and Causal Analysis for Interpretable Fault Detection in Industrial Processes
by: Santos, Pedro Cortes dos, et al.
Published: (2025)
by: Santos, Pedro Cortes dos, et al.
Published: (2025)
Estimating Heterogeneous Treatment Effects on Survival Outcomes Using Counterfactual Censoring Unbiased Transformations
by: Xu, Shenbo, et al.
Published: (2024)
by: Xu, Shenbo, et al.
Published: (2024)
A Targeted Learning Framework for Estimating Restricted Mean Survival Time Difference using Pseudo-observations
by: Jin, Man, et al.
Published: (2026)
by: Jin, Man, et al.
Published: (2026)
Nonparametric Regression Discontinuity Designs with Survival Outcomes
by: Schuessler, Maximilian, et al.
Published: (2026)
by: Schuessler, Maximilian, et al.
Published: (2026)
midr: Learning from Black-Box Models by Maximum Interpretation Decomposition
by: Asashiba, Ryoichi, et al.
Published: (2025)
by: Asashiba, Ryoichi, et al.
Published: (2025)
Reduction Techniques for Survival Analysis
by: Piller, Johannes, et al.
Published: (2025)
by: Piller, Johannes, et al.
Published: (2025)
Cellwise Outliers
by: Hubert, Mia, et al.
Published: (2026)
by: Hubert, Mia, et al.
Published: (2026)
Generalized Bayesian Ensemble Survival Tree (GBEST) model
by: Ballante, Elena, et al.
Published: (2025)
by: Ballante, Elena, et al.
Published: (2025)
Towards Accurate and Interpretable Time-series Forecasting: A Polynomial Learning Approach
by: Liu, Bo, et al.
Published: (2026)
by: Liu, Bo, et al.
Published: (2026)
Similar Items
-
Interpretable Machine Learning for Survival Analysis
by: Sophie Hanna Langbein, et al.
Published: (2025) -
survex: an R package for explaining machine learning survival models
by: Spytek, Mikołaj, et al.
Published: (2023) -
Performance is not enough: the story told by a Rashomon quartet
by: Biecek, Przemyslaw, et al.
Published: (2023) -
Functional Decomposition and Shapley Interactions for Interpreting Survival Models
by: Langbein, Sophie Hanna, et al.
Published: (2026) -
Attributions All the Way Down? The Metagame of Interpretability
by: Baniecki, Hubert, et al.
Published: (2026)