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A comparative study of methods for estimating model-agnostic Shapley value explanations
Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to …
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MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data
We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual …
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Some recent trends in embeddings of time series and dynamic networks
We give a review of some recent developments in embeddings of time series and dynamic networks. We start out with traditional principal …
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Statistical Embedding: Beyond Principal Components
There has been an intense recent activity in embedding of very high-dimensional and nonlinear data structures, much of it in the data …
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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 …
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Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual …
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Explaining predictive models using Shapley values and non-parametric vine copulas
In this paper the goal is to explain predictions from complex machine learning models. One method that has become very popular during …
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Detecting money laundering transactions with machine learning
Purpose The purpose of this paper is to develop, describe and validate a machine learning model for prioritising which financial …
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Estimating seal pup production in the Greenland Sea by using Bayesian hierarchical modelling
The Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of annual seal pup production are critical …
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Investigating mesh-based approximation methods for the normalization constant in the log Gaussian Cox process likelihood
The log Gaussian Cox process (LGCP) is a frequently applied method for modeling point pattern data. The normalization constant of the …
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