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07/25/2023
Finding Money Launderers Using Heterogeneous Graph Neural Networks
Current anti-money laundering (AML) systems, predominantly rule-based, e...
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05/16/2023
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them
Shapley values originated in cooperative game theory but are extensively...
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12/16/2022
Some recent trends in embeddings of time series and dynamic networks
We give a review of some recent developments in embeddings of time serie...
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03/23/2022
Exabel's Factor Model
Factor models have become a common and valued tool for understanding the...
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03/23/2022
Performance evaluation of volatility estimation methods for Exabel
Quantifying both historic and future volatility is key in portfolio risk...
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11/26/2021
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
Shapley values are today extensively used as a model-agnostic explanatio...
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11/18/2021
MCCE: Monte Carlo sampling of realistic counterfactual explanations
In this paper we introduce MCCE: Monte Carlo sampling of realistic Count...
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06/23/2021
groupShapley: Efficient prediction explanation with Shapley values for feature groups
Shapley values has established itself as one of the most appropriate and...
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06/03/2021
Statistical embedding: Beyond principal components
There has been an intense recent activity in embedding of very high dime...
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02/12/2021
Explaining predictive models using Shapley values and non-parametric vine copulas
The original development of Shapley values for prediction explanation re...
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07/02/2020
Explaining predictive models with mixed features using Shapley values and conditional inference trees
It is becoming increasingly important to explain complex, black-box mach...
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03/25/2019
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Explaining complex or seemingly simple machine learning models is a prac...
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08/28/2018