Saved in:
| Main Authors: | Röber, Tabea E., Lumadjeng, Adia C., Akyüz, M. Hakan, Birbil, Ş. İlker |
|---|---|
| Format: | Preprint |
| Published: |
2021
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2104.10751 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Learning with Subset Stacking
by: Birbil, Ş. İlker, et al.
Published: (2021)
by: Birbil, Ş. İlker, et al.
Published: (2021)
Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
by: Röber, Tabea E., et al.
Published: (2026)
by: Röber, Tabea E., et al.
Published: (2026)
Clinicians' Voice: Fundamental Considerations for XAI in Healthcare
by: Röber, T. E., et al.
Published: (2024)
by: Röber, T. E., et al.
Published: (2024)
Learning An Interpretable Risk Scoring System for Maximizing Decision Net Benefit
by: Chi, Wenhao, et al.
Published: (2026)
by: Chi, Wenhao, et al.
Published: (2026)
Fixing confirmation bias in feature attribution methods via semantic match
by: Cinà, Giovanni, et al.
Published: (2023)
by: Cinà, Giovanni, et al.
Published: (2023)
Generating Samples to Probe Trained Models
by: Kıral, Eren Mehmet, et al.
Published: (2025)
by: Kıral, Eren Mehmet, et al.
Published: (2025)
Scalable Bayesian Structure Learning for Gaussian Graphical Models Using Marginal Pseudo-likelihood
by: Mohammadi, Reza, et al.
Published: (2023)
by: Mohammadi, Reza, et al.
Published: (2023)
Coherent Local Explanations for Mathematical Optimization
by: Otto, Daan, et al.
Published: (2025)
by: Otto, Daan, et al.
Published: (2025)
Machine Learning for K-adaptability in Two-stage Robust Optimization
by: Julien, Esther, et al.
Published: (2022)
by: Julien, Esther, et al.
Published: (2022)
Counterfactual Explanations for Linear Optimization
by: Kurtz, Jannis, et al.
Published: (2024)
by: Kurtz, Jannis, et al.
Published: (2024)
Bolstering Stochastic Gradient Descent with Model Building
by: Birbil, S. Ilker, et al.
Published: (2021)
by: Birbil, S. Ilker, et al.
Published: (2021)
The Role of Feature Interactions in Graph-based Tabular Deep Learning
by: Dubbeldam, Elias, et al.
Published: (2025)
by: Dubbeldam, Elias, et al.
Published: (2025)
Linear Model Extraction via Factual and Counterfactual Queries
by: Otto, Daan, et al.
Published: (2026)
by: Otto, Daan, et al.
Published: (2026)
Learning Interpretable Rules for Scalable Data Representation and Classification
by: Wang, Zhuo, et al.
Published: (2023)
by: Wang, Zhuo, et al.
Published: (2023)
Does Interpretability of Knowledge Tracing Models Support Teacher Decision Making?
by: Khalid, Adia, et al.
Published: (2025)
by: Khalid, Adia, et al.
Published: (2025)
Output-Constrained Decision Trees
by: Tunç, Hüseyin, et al.
Published: (2024)
by: Tunç, Hüseyin, et al.
Published: (2024)
Classification Filtering
by: Bayram, Ilker
Published: (2025)
by: Bayram, Ilker
Published: (2025)
From Large Language Models and Optimization to Decision Optimization CoPilot: A Research Manifesto
by: Wasserkrug, Segev, et al.
Published: (2024)
by: Wasserkrug, Segev, et al.
Published: (2024)
Constraint-Aware Refinement for Safety Verification of Neural Feedback Loops
by: Rober, Nicholas, et al.
Published: (2024)
by: Rober, Nicholas, et al.
Published: (2024)
(Sometimes) Less is More: Mitigating the Complexity of Rule-based Representation for Interpretable Classification
by: Bergamin, Luca, et al.
Published: (2025)
by: Bergamin, Luca, et al.
Published: (2025)
Interpretable Classification via a Rule Network with Selective Logical Operators
by: Wei, Bowen, et al.
Published: (2024)
by: Wei, Bowen, et al.
Published: (2024)
Interpretable Fair Clustering
by: Jiang, Mudi, et al.
Published: (2025)
by: Jiang, Mudi, et al.
Published: (2025)
LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification
by: Andrews, Thomas, et al.
Published: (2025)
by: Andrews, Thomas, et al.
Published: (2025)
Fair MP-BOOST: Fair and Interpretable Minipatch Boosting
by: Little, Camille Olivia, et al.
Published: (2024)
by: Little, Camille Olivia, et al.
Published: (2024)
Scalable Rule Lists Learning with Sampling
by: Pellegrina, Leonardo, et al.
Published: (2024)
by: Pellegrina, Leonardo, et al.
Published: (2024)
FairGLVQ: Fairness in Partition-Based Classification
by: Störck, Felix, et al.
Published: (2024)
by: Störck, Felix, et al.
Published: (2024)
Mitigating Bias in Concept Bottleneck Models for Fair and Interpretable Image Classification
by: Tong, Schrasing, et al.
Published: (2026)
by: Tong, Schrasing, et al.
Published: (2026)
When Interpretability Is Unequally Distributed: Fairness in Hybrid Interpretable Models
by: Zare, Ziba Jabbar, et al.
Published: (2026)
by: Zare, Ziba Jabbar, et al.
Published: (2026)
A General Anchor-Based Framework for Scalable Fair Clustering
by: Wei, Shengfei, et al.
Published: (2025)
by: Wei, Shengfei, et al.
Published: (2025)
Efficient and Interpretable Transformer for Counterfactual Fairness
by: Dong, Panyi, et al.
Published: (2026)
by: Dong, Panyi, et al.
Published: (2026)
Learning Interpretable Fair Representations
by: Wang, Tianhao, et al.
Published: (2024)
by: Wang, Tianhao, et al.
Published: (2024)
FairGP: A Scalable and Fair Graph Transformer Using Graph Partitioning
by: Luo, Renqiang, et al.
Published: (2024)
by: Luo, Renqiang, et al.
Published: (2024)
FSD-Inference: Fully Serverless Distributed Inference with Scalable Cloud Communication
by: Oakley, Joe, et al.
Published: (2024)
by: Oakley, Joe, et al.
Published: (2024)
NEUROLOGIC: From Neural Representations to Interpretable Logic Rules
by: Geng, Chuqin, et al.
Published: (2025)
by: Geng, Chuqin, et al.
Published: (2025)
Finding Rule-Interpretable Non-Negative Data Representation
by: Mihelčić, Matej, et al.
Published: (2022)
by: Mihelčić, Matej, et al.
Published: (2022)
Pitfalls of Conformal Predictions for Medical Image Classification
by: Mehrtens, Hendrik, et al.
Published: (2025)
by: Mehrtens, Hendrik, et al.
Published: (2025)
FairZK: A Scalable System to Prove Machine Learning Fairness in Zero-Knowledge
by: Zhang, Tianyu, et al.
Published: (2025)
by: Zhang, Tianyu, et al.
Published: (2025)
Personalized Interpretable Classification
by: He, Zengyou, et al.
Published: (2023)
by: He, Zengyou, et al.
Published: (2023)
Interpretable Hybrid-Rule Temporal Point Processes
by: Cao, Yunyang, et al.
Published: (2025)
by: Cao, Yunyang, et al.
Published: (2025)
A Logical-Rule Autoencoder for Interpretable Recommendations
by: Pan, Jinhao, et al.
Published: (2026)
by: Pan, Jinhao, et al.
Published: (2026)
Similar Items
-
Learning with Subset Stacking
by: Birbil, Ş. İlker, et al.
Published: (2021) -
Improving understanding and trust in AI: How users benefit from interval-based counterfactual explanations
by: Röber, Tabea E., et al.
Published: (2026) -
Clinicians' Voice: Fundamental Considerations for XAI in Healthcare
by: Röber, T. E., et al.
Published: (2024) -
Learning An Interpretable Risk Scoring System for Maximizing Decision Net Benefit
by: Chi, Wenhao, et al.
Published: (2026) -
Fixing confirmation bias in feature attribution methods via semantic match
by: Cinà, Giovanni, et al.
Published: (2023)