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Jie Chen

Wells Fargo

United States

SCHOLARLY PAPERS

5

DOWNLOADS

1,905

TOTAL CITATIONS

32

Scholarly Papers (5)

1.

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)

Abstract:

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

2.

Time Series Simulation by Conditional Generative Adversarial Net

Number of pages: 33 Posted: 17 May 2019
Wells Fargo, Wells Fargo, Wells Fargo, Wells Fargo Bank and Corporate Model Risk, Wells Fargo Bank
Downloads 429 (169,823)
Citation 23

Abstract:

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Conditional Generative Adversarial Net, Neural Network, Time Series, Market and Credit Risk Management

3.

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

4.

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

5.

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)

Abstract:

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