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Found 12 papers, 5 papers with code
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them
The method classes use either Monte Carlo integration or regression to model the conditional expectations.
Multimodal Generative Models for Bankruptcy Prediction Using Textual Data
To solve this limitation, this research introduces the Conditional Multimodal Discriminative (CMMD) model that learns multimodal representations that embed information from accounting, market, and textual data modalities.
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models.
MCCE: Monte Carlo sampling of realistic counterfactual explanations
We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual explanation method that generates on-manifold, actionable and valid counterfactuals by modeling the joint distribution of the mutable features given the immutable features and the decision.
Discriminative Multimodal Learning via Conditional Priors in Generative Models
Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data.
groupShapley: Efficient prediction explanation with Shapley values for feature groups
The main drawback with Shapley values, however, is that its computational complexity grows exponentially in the number of input features, making it unfeasible in many real world situations where there could be hundreds or thousands of features.
Explaining predictive models using Shapley values and non-parametric vine copulas
In this paper we propose two new approaches for modelling the dependence between the features.
Explaining predictive models with mixed features using Shapley values and conditional inference trees
It is becoming increasingly important to explain complex, black-box machine learning models.
Deep Generative Models for Reject Inference in Credit Scoring
Reject inference is the process of attempting to infer the creditworthiness status of the rejected applications.
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
In this paper, we extend the Kernel SHAP method to handle dependent features.
Learning Latent Representations of Bank Customers With The Variational Autoencoder
We show that it is possible to steer the latent representations in the latent space of the VAE using the Weight of Evidence and forming a specific grouping of the data that reflects the customers' creditworthiness.
Segment-Based Credit Scoring Using Latent Clusters in the Variational Autoencoder
We use the VAE and show that transforming the input data into a meaningful representation, it is possible to steer configurations in the latent space of the VAE.
