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Vijay Nair

Corporate Model Risk, Wells Fargo

301 South Tryon Street

Wells Faro Three 10th Floor

Charlotte, NC 28288

United States

SCHOLARLY PAPERS

6

DOWNLOADS
Rank 35,625

SSRN RANKINGS

Top 35,625

in Total Papers Downloads

3,573

TOTAL CITATIONS

17

Scholarly Papers (6)

1.

Model Robustness With Text Classification: Semantic-Preserving Adversarial Attacks

Number of pages: 12 Posted: 08 Oct 2020
Corporate Model Risk, Wells Fargo, Wells Fargo, Corporate Model Risk, Wells Fargo and Corporate Model Risk, Wells Fargo Bank
Downloads 1,069 (51,624)
Citation 2

Abstract:

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Machine Learning, Natural Language Processing, Robustness, Adversarial Testing

2.

Quantifying Inherent Randomness in Machine Learning Algorithms

Number of pages: 14 Posted: 11 Jul 2022
Corporate Model Risk, Wells Fargo, Corporate Model Risk, Wells Fargo, Wells Fargo and Corporate Model Risk, Wells Fargo
Downloads 1,028 (54,708)
Citation 6

Abstract:

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Model Training, Reproducibility, Variation

3.

Explaining Adverse Actions in Credit Decisions Using Shapley Decomposition

Number of pages: 20 Posted: 03 May 2022
Corporate Model Risk, Wells Fargo, - Corporate Model Risk Management, affiliation not provided to SSRN, - Corporate Model Risk Management, Wells Fargo and Corporate Model Risk, Wells Fargo Bank
Downloads 811 (75,476)

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Baseline Shapley, explainability, machine learning, model-agnostic interpretation

4.

Adaptive Explainable Neural Networks (Axnns)

Number of pages: 22 Posted: 06 May 2020
Wells Fargo, Wells Fargo, Corporate Model Risk, Wells Fargo and Corporate Model Risk, Wells Fargo Bank
Downloads 404 (181,553)
Citation 9

Abstract:

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Additive Index Models, Boosting, Generalized Additive Models, Interpret-able Machine Learning, Main Effects and Interactions, Stacking

5.

Interpretable Feature Engineering for Time Series Predictors using Attention Networks

Number of pages: 19 Posted: 01 Jun 2022
- Corporate Model Risk Management, Wells Fargo, Wells Fargo and Corporate Model Risk, Wells Fargo
Downloads 149 (502,974)

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Attention heads, Deep neural networks, Interpretable feature engineering

6.

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

Number of pages: 60 Posted: 29 Apr 2022 Last Revised: 05 May 2022
2nd Order Solutions, affiliation not provided to SSRN, affiliation not provided to SSRN, Wells Fargo and Corporate Model Risk, Wells Fargo
Downloads 112 (635,507)

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supervised machine learning algorithms, random forest, gradient boosting machines, xgboost, feedforward neural network, interpretability, model performance