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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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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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12/15/2020
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
In recent years, the field of recommendation systems has attracted incre...
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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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09/08/2020
Hyperparameter Optimization via Sequential Uniform Designs
Hyperparameter tuning or optimization plays a central role in the automa...
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06/08/2020
Balance-Subsampled Stable Prediction
In machine learning, it is commonly assumed that training and test data ...
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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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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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02/20/2019
Adaptive Iterative Hessian Sketch via A-Optimal Subsampling
Iterative Hessian sketch (IHS) is an effective sketching method for mode...
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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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08/21/2018
Interval-valued Data Prediction via Regularized Artificial Neural Network
A regularized artificial neural network (RANN) is proposed for interval-...
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08/17/2018
Mixed-Level Column Augmented Uniform Designs
In follow-up designs, some additional factors with two or three levels m...
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09/19/2017
