Search Results for author: Vijayan N. Nair

Found 21 papers, 0 papers with code

Monotone Tree-Based GAMI Models by Adapting XGBoost

no code implementations5 Sep 2023 Linwei Hu, Soroush Aramideh, Jie Chen, Vijayan N. Nair

It is straightforward to fit a monotone model to $f(x)$ using the options in XGBoost.

Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons

no code implementations25 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.

Interpretable Machine Learning

Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation

no code implementations15 Nov 2022 Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair

Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance.

Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights

no code implementations12 Aug 2022 Tianshu Feng, Zhipu Zhou, Joshi Tarun, Vijayan N. Nair

There are many different methods in the literature for local explanation of machine learning results.

Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models

no code implementations14 Jul 2022 Linwei Hu, Jie Chen, Vijayan N. Nair

We propose a new algorithm, called GAMI-Tree, that is similar to EBM, but has a number of features that lead to better performance.

BIG-bench Machine Learning Interpretable Machine Learning

Quantifying Inherent Randomness in Machine Learning Algorithms

no code implementations24 Jun 2022 Soham Raste, Rahul Singh, Joel Vaughan, Vijayan N. Nair

Among the different algorithms, randomness in model training causes larger variation for FFNNs compared to tree-based methods.

BIG-bench Machine Learning

Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study

no code implementations27 Apr 2022 Alice J. Liu, Arpita Mukherjee, Linwei Hu, Jie Chen, Vijayan N. Nair

Overall, XGB and FFNNs were competitive, with FFNNs showing better performance in smooth models and tree-based boosting algorithms performing better in non-smooth models.

BIG-bench Machine Learning

Self-interpretable Convolutional Neural Networks for Text Classification

no code implementations18 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.

text-classification Text Classification

Bias, Fairness, and Accountability with AI and ML Algorithms

no code implementations13 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.

Fairness

Recent Trends in the Use of Deep Learning Models for Grammar Error Handling

no code implementations4 Sep 2020 Mina Naghshnejad, Tarun Joshi, Vijayan N. Nair

Additionally, we discuss different techniques to improve the performance of these models at each stage of the pipeline.

Machine Translation Translation

Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms

no code implementations28 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.

BIG-bench Machine Learning regression

Adaptive Explainable Neural Networks (AxNNs)

no code implementations5 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.

Distributed Computing

Explainable Neural Networks based on Additive Index Models

no code implementations5 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.

Feature Engineering

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