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Found 25 papers, 4 papers with code
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons
To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations.
PiML Toolbox for Interpretable Machine Learning Model Development and Diagnostics
PiML (read $\pi$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics.
Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularization
Gradient-boosted decision trees (GBDT) are widely used and highly effective machine learning approach for tabular data modeling.
Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition
The applicant is then entitled to an explanation for the negative decision.
Traversing the Local Polytopes of ReLU Neural Networks
Our polytope traversing algorithm can be adapted to a wide range of applications related to robustness and interpretability.
Traversing the Local Polytopes of ReLU Neural Networks: A Unified Approach for Network Verification
Although neural networks (NNs) with ReLU activation functions have found success in a wide range of applications, their adoption in risk-sensitive settings has been limited by the concerns on robustness and interpretability.
Designing Inherently Interpretable Machine Learning Models
Interpretable machine learning (IML) becomes increasingly important in highly regulated industry sectors related to the health and safety or fundamental rights of human beings.
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling.
Self-interpretable Convolutional Neural Networks for Text Classification
We also study the impact of the complexity of the convolutional layers and the classification layers on the model performance.
Bias, Fairness, and Accountability with AI and ML Algorithms
In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms.
Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model
The LIFE algorithm is able to fit a wide single-hidden-layer neural network (NN) accurately with three steps: defining the subsets of a dataset by the linear projections of neural nodes, creating the features from multiple narrow single-hidden-layer NNs trained on the different subsets of the data, combining the features with a linear model.
Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
We propose the local linear profile plot and other visualization methods for interpretation and diagnostics, and an effective merging strategy for network simplification.
SHAP values for Explaining CNN-based Text Classification Models
Deep neural networks are increasingly used in natural language processing (NLP) models.
Model Robustness with Text Classification: Semantic-preserving adversarial attacks
We propose algorithms to create adversarial attacks to assess model robustness in text classification problems.
Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, have become popular in recent years due to their superior predictive performance over traditional statistical methods.
Supervised Machine Learning Techniques: An Overview with Applications to Banking
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking.
An Effective and Efficient Initialization Scheme for Training Multi-layer Feedforward Neural Networks
Network initialization is the first and critical step for training neural networks.
Adaptive Explainable Neural Networks (AxNNs)
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the results.
GAMI-Net: An Explainable Neural Network based on Generalized Additive Models with Structured Interactions
The lack of interpretability is an inevitable problem when using neural network models in real applications.
Time Series Simulation by Conditional Generative Adversarial Net
Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation.
Deep-learning based numerical BSDE method for barrier options
In this approach, deep learning is used to learn some deterministic functions, which are used in solving the BSDE with terminal conditions.
Enhancing Explainability of Neural Networks through Architecture Constraints
It leads to an explainable neural network (xNN) with the superior balance between prediction performance and model interpretability.
Model Interpretation: A Unified Derivative-based Framework for Nonparametric Regression and Supervised Machine Learning
Interpreting a nonparametric regression model with many predictors is known to be a challenging problem.
Explainable Neural Networks based on Additive Index Models
In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features.
Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP)
This is in contrast with the KLIME approach that is based on clustering the predictor space.
