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 • 26 Apr 2022 • Vijayan N. Nair, Tianshu Feng, Linwei Hu, Zach Zhang, Jie Chen, Agus Sudjianto
The applicant is then entitled to an explanation for the negative decision.
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 • 9 Sep 2021 • Shaojie Xu, Joel Vaughan, Jie Chen, Agus Sudjianto, Vijayan Nair
Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling.
no code implementations • 18 May 2021 • Wei Zhao, Rahul Singh, Tarun Joshi, Agus Sudjianto, Vijayan N. Nair
We also study the impact of the complexity of the convolutional layers and the classification layers on the model performance.
no code implementations • 13 May 2021 • Nengfeng Zhou, Zach Zhang, Vijayan N. Nair, Harsh Singhal, Jie Chen, Agus Sudjianto
In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms.
no code implementations • 18 Mar 2021 • Agus Sudjianto, Jinwen Qiu, Miaoqi Li, Jie Chen
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.
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.
no code implementations • 26 Aug 2020 • Wei Zhao, Tarun Joshi, Vijayan N. Nair, Agus Sudjianto
Deep neural networks are increasingly used in natural language processing (NLP) models.
no code implementations • 12 Aug 2020 • Rahul Singh, Tarun Joshi, Vijayan N. Nair, Agus Sudjianto
We propose algorithms to create adversarial attacks to assess model robustness in text classification problems.
no code implementations • 28 Jul 2020 • Linwei Hu, Jie Chen, Vijayan N. Nair, Agus Sudjianto
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.
no code implementations • 28 Jul 2020 • Linwei Hu, Jie Chen, Joel Vaughan, Hanyu Yang, Kelly Wang, Agus Sudjianto, Vijayan N. Nair
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking.
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.
no code implementations • 5 Apr 2020 • Jie Chen, Joel Vaughan, Vijayan N. Nair, Agus Sudjianto
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.
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 • 25 Apr 2019 • Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto
Generative Adversarial Net (GAN) has been proven to be a powerful machine learning tool in image data analysis and generation.
no code implementations • 11 Apr 2019 • Bing Yu, Xiaojing Xing, Agus Sudjianto
In this approach, deep learning is used to learn some deterministic functions, which are used in solving the BSDE with terminal conditions.
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.
no code implementations • 22 Aug 2018 • Xiaoyu Liu, Jie Chen, Joel Vaughan, Vijayan Nair, Agus Sudjianto
Interpreting a nonparametric regression model with many predictors is known to be a challenging problem.
no code implementations • 5 Jun 2018 • Joel Vaughan, Agus Sudjianto, Erind Brahimi, Jie Chen, Vijayan N. Nair
In this paper, we present the Explainable Neural Network (xNN), a structured neural network designed especially to learn interpretable features.
no code implementations • 2 Jun 2018 • Linwei Hu, Jie Chen, Vijayan N. Nair, Agus Sudjianto
This is in contrast with the KLIME approach that is based on clustering the predictor space.