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05/25/2023
Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons
In the early days of machine learning (ML), the emphasis was on developi...
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05/07/2023
PiML Toolbox for Interpretable Machine Learning Model Development and Validation
PiML (read π-ML, /`pai.`em.`el/) is an integrated and open-access Python...
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04/26/2023
Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularization
Gradient-boosted decision trees (GBDT) are widely used and highly effect...
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04/26/2022
Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition
When a financial institution declines an application for credit, an adve...
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11/17/2021
Traversing the Local Polytopes of ReLU Neural Networks: A Unified Approach for Network Verification
Although neural networks (NNs) with ReLU activation functions have found...
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11/02/2021
Designing Inherently Interpretable Machine Learning Models
Interpretable machine learning (IML) becomes increasingly important in h...
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09/09/2021
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
Principal component analysis (PCA) is a well-known linear dimension-redu...
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05/18/2021
Self-interpretable Convolutional Neural Networks for Text Classification
Deep learning models for natural language processing (NLP) are inherentl...
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05/13/2021
Bias, Fairness, and Accountability with AI and ML Algorithms
The advent of AI and ML algorithms has led to opportunities as well as c...
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03/18/2021
Linear Iterative Feature Embedding: An Ensemble Framework for Interpretable Model
A new ensemble framework for interpretable model called Linear Iterative...
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11/08/2020
Unwrapping The Black Box of Deep ReLU Networks: Interpretability, Diagnostics, and Simplification
The deep neural networks (DNNs) have achieved great success in learning ...
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08/26/2020
SHAP values for Explaining CNN-based Text Classification Models
Deep neural networks are increasingly used in natural language processin...
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08/12/2020
Model Robustness with Text Classification: Semantic-preserving adversarial attacks
We propose algorithms to create adversarial attacks to assess model robu...
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07/28/2020
Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms
Supervised Machine Learning (SML) algorithms, such as Gradient Boosting,...
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05/16/2020
An Effective and Efficient Initialization Scheme for Training Multi-layer Feedforward Neural Networks
Network initialization is the first and critical step for training neura...
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05/16/2020
An Effective and Efficient Initialization Scheme for Multi-layer Feedforward Neural Networks
Network initialization is the first and critical step for training neura...
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05/16/2020
An Effective and Efficient Training Algorithm for Multi-layer Feedforward Neural Networks
Network initialization is the first and critical step for training neura...
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04/05/2020
Adaptive Explainable Neural Networks (AxNNs)
While machine learning techniques have been successfully applied in seve...
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03/16/2020
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 ...
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04/25/2019
Time Series Simulation by Conditional Generative Adversarial Net
Generative Adversarial Net (GAN) has been proven to be a powerful machin...
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01/12/2019
Enhancing Explainability of Neural Networks through Architecture Constraints
Prediction accuracy and model explainability are the two most important ...
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08/22/2018
Model Interpretation: A Unified Derivative-based Framework for Nonparametric Regression and Supervised Machine Learning
Interpreting a nonparametric regression model with many predictors is kn...
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06/05/2018
Explainable Neural Networks based on Additive Index Models
Machine Learning algorithms are increasingly being used in recent years ...
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06/02/2018
