Search Results for author:
Found 15 papers, 5 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.
Model-free Subsampling Method Based on Uniform Designs
In order to measure the goodness of representation of a subdata with respect to the original data, we propose a criterion, generalized empirical F-discrepancy (GEFD), and study its theoretical properties in connection with the classical generalized L2-discrepancy in the theory of uniform designs.
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.
Explainable Recommendation Systems by Generalized Additive Models with Manifest and Latent Interactions
In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI).
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.
Hyperparameter Optimization via Sequential Uniform Designs
Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML).
Balance-Subsampled Stable Prediction
However, this assumption is often violated in practice because the sample selection bias may induce the distribution shift from training data to test data.
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.
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.
Adaptive Iterative Hessian Sketch via A-Optimal Subsampling
Iterative Hessian sketch (IHS) is an effective sketching method for modeling large-scale data.
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.
