Vijayan N. Nair

35 papers Journal 35

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Assessing Robustness of Machine Learning Models using Covariate Perturbations. CoRR Arun Prakash R, Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair
2024	J	`jnl`	Cross Spline Net and a Unified World. CoRR Linwei Hu, Ye Jin Choi, Vijayan N. Nair
2023	J	`jnl`	Document Automation Architectures: Updated Survey in Light of Large Language Models. CoRR Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi, Vijayan N. Nair
2023	J	`jnl`	Interpretable Machine Learning based on Functional ANOVA Framework: Algorithms and Comparisons. CoRR Linwei Hu, Vijayan N. Nair, Agus Sudjianto, Aijun Zhang, Jie Chen
2023	J	`jnl`	Monotone Tree-Based GAMI Models by Adapting XGBoost. CoRR Linwei Hu, Soroush Aramideh, Jie Chen, Vijayan N. Nair
2022	J	`jnl`	Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation. CoRR Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair
2022	J	`jnl`	Comparing Baseline Shapley and Integrated Gradients for Local Explanation: Some Additional Insights. CoRR Tianshu Feng, Zhipu Zhou, Tarun Joshi, Vijayan N. Nair
2022	J	`jnl`	Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition. CoRR Vijayan N. Nair, Tianshu Feng, Linwei Hu, Zach Zhang, Jie Chen, Agus Sudjianto
2022	J	`jnl`	Interpretable Feature Engineering for Time Series Predictors using Attention Networks. CoRR Tianjie Wang, Jie Chen, Joel Vaughan, Vijayan N. Nair
2022	J	`jnl`	Performance and Interpretability Comparisons of Supervised Machine Learning Algorithms: An Empirical Study. CoRR Alice J. Liu, Linwei Hu, Jie Chen, Vijayan N. Nair
2022	J	`jnl`	Quantifying Inherent Randomness in Machine Learning Algorithms. CoRR Soham Raste, Rahul Singh, Joel Vaughan, Vijayan N. Nair
2022	J	`jnl`	Using Model-Based Trees with Boosting to Fit Low-Order Functional ANOVA Models. CoRR Linwei Hu, Jie Chen, Vijayan N. Nair
2021	J	`jnl`	Bias, Fairness, and Accountability with AI and ML Algorithms. CoRR Nengfeng Zhou, Zach Zhang, Vijayan N. Nair, Harsh Singhal, Jie Chen, Agus Sudjianto
2021	J	`jnl`	Document Automation Architectures and Technologies: A Survey. CoRR Mohammad Ahmadi Achachlouei, Omkar Patil, Tarun Joshi, Vijayan N. Nair
2021	J	`jnl`	Self-interpretable Convolutional Neural Networks for Text Classification. CoRR Wei Zhao, Rahul Singh, Tarun Joshi, Agus Sudjianto, Vijayan N. Nair
2021	J	`jnl`	Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons. CoRR Shaojie Xu, Joel Vaughan, Jie Chen, Agus Sudjianto, Vijayan N. Nair
2020	J	`jnl`	Adaptive Explainable Neural Networks (AxNNs). CoRR Jie Chen, Joel Vaughan, Vijayan N. Nair, Agus Sudjianto
2020	J	`jnl`	Model Robustness with Text Classification: Semantic-preserving adversarial attacks. CoRR Rahul Singh, Tarun Joshi, Vijayan N. Nair, Agus Sudjianto
2020	J	`jnl`	Recent Trends in the Use of Deep Learning Models for Grammar Error Handling. CoRR Mina Naghshnejad, Tarun Joshi, Vijayan N. Nair
2020	J	`jnl`	SHAP values for Explaining CNN-based Text Classification Models. CoRR Wei Zhao, Tarun Joshi, Vijayan N. Nair, Agus Sudjianto
2020	J	`jnl`	Supervised Machine Learning Techniques: An Overview with Applications to Banking. CoRR Linwei Hu, Jie Chen, Joel Vaughan, Hanyu Yang, Kelly Wang, Agus Sudjianto, Vijayan N. Nair
2020	J	`jnl`	Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms. CoRR Linwei Hu, Jie Chen, Vijayan N. Nair, Agus Sudjianto
2018	J	`jnl`	Explainable Neural Networks based on Additive Index Models. CoRR Joel Vaughan, Agus Sudjianto, Erind Brahimi, Jie Chen, Vijayan N. Nair
2018	J	`jnl`	Locally Interpretable Models and Effects based on Supervised Partitioning (LIME-SUP). CoRR Linwei Hu, Jie Chen, Vijayan N. Nair, Agus Sudjianto
2018	J	`jnl`	Model Interpretation: A Unified Derivative-based Framework for Nonparametric Regression and Supervised Machine Learning. CoRR Xiaoyu Liu, Jie Chen, Vijayan N. Nair, Agus Sudjianto
2012	J	`jnl`	Noise Variable Settings in Robust Design Experiments. Technometrics Derek Bingham, Vijayan N. Nair
2008	J	`jnl`	Bayesian Inference for Multivariate Ordinal Data Using Parameter Expansion. Technometrics Earl Lawrence, Derek Bingham, Chuanhai Liu, Vijayan N. Nair
2007	J	`jnl`	Graphical Estimators From Probability Plots With Right-Censored Data. Technometrics Anupap Somboonsavatdee, Vijayan N. Nair, Ananda Sen
2007	J	`jnl`	Statistical Aspects of the Analysis of Data Networks. Technometrics Lorraine Denby, Jim Landwehr, Colin L. Mallows, Jean Meloche, John Tuck, Bowei Xi, George Michailidis, Vijayan N. Nair
2006	J	`jnl`	Extreme (X-)Testing With Binary Data and Applications to Reliability Demonstration. Technometrics David Mease, Vijayan N. Nair
2004	J	`jnl`	Discussion. Technometrics Vijayan N. Nair, Xiao Wang
2004	J	`jnl`	Selective Assembly in Manufacturing: Statistical Issues and Optimal Binning Strategies. Technometrics David Mease, Vijayan N. Nair, Agus Sudjianto
2002	J	`jnl`	Proportional Integral Derivative Charts for Process Monitoring. Technometrics Wei Jiang, Huaiqing Wu, Fugee Tsung, Vijayan N. Nair, Kwok-Leung Tsui
2001	J	`jnl`	Methods for Identifying Dispersion Effects in Unreplicated Factorial Experiments - A Critical Analysis and Proposed Strategies. Technometrics William A. Brenneman, Vijayan N. Nair
1985	J	`jnl`	Optimal Sequential Selection of Secretaries. Math. Oper. Res. Colin L. Mallows, Vijayan N. Nair, Larry A. Shepp, Yehuda Vardi