no code implementations • 25 May 2023 • Linwei Hu, Vijayan N. Nair, Agus Sudjianto, Aijun Zhang, Jie Chen
To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations.
1 code implementation • 7 May 2023 • Agus Sudjianto, Aijun Zhang, Zebin Yang, Yu Su, Ningzhou Zeng
PiML (read $\pi$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics.
no code implementations • 26 Apr 2023 • Shijie Cui, Agus Sudjianto, Aijun Zhang, Runze Li
Gradient-boosted decision trees (GBDT) are widely used and highly effective machine learning approach for tabular data modeling.
no code implementations • 8 Sep 2022 • Mei Zhang, Yongdao Zhou, Zheng Zhou, Aijun Zhang
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.
no code implementations • AAAI Workshop AdvML 2022 • Shaojie Xu, Joel Vaughan, Jie Chen, Aijun Zhang, Agus Sudjianto
Our polytope traversing algorithm can be adapted to a wide range of applications related to robustness and interpretability.
no code implementations • 17 Nov 2021 • Shaojie Xu, Joel Vaughan, Jie Chen, Aijun Zhang, Agus Sudjianto
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.
1 code implementation • 2 Nov 2021 • Agus Sudjianto, Aijun Zhang
Interpretable machine learning (IML) becomes increasingly important in highly regulated industry sectors related to the health and safety or fundamental rights of human beings.
no code implementations • 15 Dec 2020 • Yifeng Guo, Yu Su, Zebin Yang, Aijun Zhang
In this paper, we propose the explainable recommendation systems based on a generalized additive model with manifest and latent interactions (GAMMLI).
1 code implementation • 8 Nov 2020 • Agus Sudjianto, William Knauth, Rahul Singh, Zebin Yang, Aijun Zhang
We propose the local linear profile plot and other visualization methods for interpretation and diagnostics, and an effective merging strategy for network simplification.
2 code implementations • 8 Sep 2020 • Zebin Yang, Aijun Zhang
Hyperparameter optimization (HPO) plays a central role in the automated machine learning (AutoML).
no code implementations • 8 Jun 2020 • Kun Kuang, Hengtao Zhang, Fei Wu, Yueting Zhuang, Aijun Zhang
However, this assumption is often violated in practice because the sample selection bias may induce the distribution shift from training data to test data.
no code implementations • 16 May 2020 • Zebin Yang, Hengtao Zhang, Agus Sudjianto, Aijun Zhang
Network initialization is the first and critical step for training neural networks.
2 code implementations • 16 Mar 2020 • Zebin Yang, Aijun Zhang, Agus Sudjianto
The lack of interpretability is an inevitable problem when using neural network models in real applications.
no code implementations • 20 Feb 2019 • Aijun Zhang, Hengtao Zhang, Guosheng Yin
Iterative Hessian sketch (IHS) is an effective sketching method for modeling large-scale data.
no code implementations • 12 Jan 2019 • Zebin Yang, Aijun Zhang, Agus Sudjianto
It leads to an explainable neural network (xNN) with the superior balance between prediction performance and model interpretability.