This family of models is self-explained and transparent to users.
On the Power of Decision Trees in Auto-Regressive Language Modeling, NeurIPS 2024
PICNN: A Pathway towards Interpretable Convolutional Neural Networks, AAAI 2024
Self-Interpretable Graph Learning with Sufficient and Necessary Explanations, AAAI 2024
Learning Performance Maximizing Ensembles with Explainability Guarantees, AAAI 2024
NeSyFOLD: A Framework for Interpretable Image Classification, AAAI 2024
Pantypes: Diverse Representatives for Self-Explainable Models, AAAI 2024
Towards Modeling Uncertainties of Selfexplaining Neural Networks, AAAI 2024
FEAMOE: Fair, Explainable and Adaptive Mixture of Experts, IJCAI 2023
Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint, ICLR 2023
A Framework for Learning Ante-hoc Explainable Models via Concepts, CVPR 2022
Explainable Reinforcement Learning via Model Transforms, NeurIPS 2022
Visual correspondence-based explanations improve AI robustness and human-AI team accuracy, NeurIPS 2022
Decision Trees with Short Explainable Rules, NeurIPS 2022
Hierarchical Shrinkage:improving the accuracy and interpretability of tree-based methods, ICML 2022
Entropy-based Logic Explanations of Neural Networks, AAAI 2022
Scalable Rule-Based Representation Learning for Interpretable Classification, NeurIPS 2021
Neural Additive Models: Interpretable Machine Learning with Neural Nets, NeurIPS 2021
Self-Interpretable Model with TransformationEquivariant Interpretation, NeurIPS 2021
Interpretable Compositional Convolutional Neural Networks, IJCAI 2021
Connecting Interpretability and Robustness in Decision Trees through Separation, ICML 2021
TabNet: Attentive Interpretable Tabular Learning, AAAI 2021
Building Interpretable Interaction Trees for Deep NLP Mode, AAAI 2021
Shapley Explanation Networks, ICLR 2021
NBDT: Neural-Backed Decision Trees, ICLR 2021
Neural additive models: Interpretable machine learning with neural nets, Arxiv preprint 2020
Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters, ECCV 2020
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization, AAAI 2020
Generalized Linear Rule Models, ICML 2019
Axiomatic Interpretability for Multiclass Additive Models, KDD 2019
Interpretable Convolutional Neural Networks, CVPR 2018
Boolean Decision Rules via Column Generation, NIPS 2018
Towards Robust Interpretability with Self-Explaining Neural Networks, NIPS 2018
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model, The Annals of Applied Statistics 2015
Towards Robust Interpretability with Self-Explaining Neural Networks, NeurIPS 2016
Comprehensible Classification Models – a position paper, KDD 2015
Making machine learning models interpretable, ESANN 2012
Predictive learning via rule ensembles, The Annals of Applied Statistics 2008
Other transparent models:
- Decision Tree
- Linear Models
- Rule-based Models
- K-NN
- General Additive Models(GAMs)
- RuleFit