22 citations · 42 across the 20 of their papers we have counts for
22 papers
Scope-Restricted Backtracking Counterfactuals
Johan de Aguas, Martin Jullum
Interventional counterfactuals evaluate statements such as "had been " by replacing the assignment for while keeping the unit's exogenous background fixed. Backtracking…
FindMyText: Robust, Scalable Detection of Text Containment in Large Web-Crawled Corpora
Lars Henry Berge Olsen, Pierre Lison, Martin Jullum +1
We present FindMyText, an open-source Python package designed to efficiently assess whether a given text appears, in part or in full, within a text corpus. The tool builds on prior…
Beyond Defensive Reporting: Machine Learning for Active Anti-Money Laundering Control in Insurance
Dara Goldar, Geir Kjetil Ferkingstad Sandve, Martin Jullum
Money laundering through insurance claims poses a threat to insurers both through fraudulent payouts and reputational and regulatory risk. Despite this, little research has examine…
How important are the genes to explain the outcome - the asymmetric Shapley value as an honest importance metric for high-dimensional features
Mark A. van de Wiel, Jeroen Goedhart, Martin Jullum +1
In clinical prediction settings the importance of a high-dimensional feature like genomics is often assessed by evaluating the change in predictive performance when adding it to a…
Parametric or nonparametric: the FIC approach for stationary time series
Gudmund Hermansen, Nils Lid Hjort, Martin Jullum
We seek to narrow the gap between parametric and nonparametric modelling of stationary time series processes. The approach is inspired by recent advances in focused inference and m…
AI/ML for 5G and Beyond Cybersecurity
Sandeep Pirbhulal, Habtamu Abie, Martin Jullum +2
The advancements in communication technology (5G and beyond) and global connectivity Internet of Things (IoT) also come with new security problems that will need to be addressed in…
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
Jan Kapar, Niklas Koenen, Martin Jullum
Evaluating synthetic tabular data is challenging, since they can differ from the real data in so many ways. There exist numerous metrics of synthetic data quality, ranging from sta…
shapr: Explaining Machine Learning Models with Conditional Shapley Values in R and Python
Martin Jullum, Lars Henry Berge Olsen, Jon Lachmann +1
This paper introduces the shapr R package, a versatile tool for generating Shapley value-based prediction explanations for machine learning and statistical regression models. Moreo…
Improving the Weighting Strategy in KernelSHAP
Lars Henry Berge Olsen, Martin Jullum
In Explainable AI (XAI), Shapley values are a popular model-agnostic framework for explaining predictions made by complex machine learning models. The computation of Shapley values…
Finding Money Launderers Using Heterogeneous Graph Neural Networks
Fredrik Johannessen, Martin Jullum
Current anti-money laundering (AML) systems, predominantly rule-based, exhibit notable shortcomings in efficiently and precisely detecting instances of money laundering. As a resul…
A Comparative Study of Methods for Estimating Conditional Shapley Values and When to Use Them
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum +1
Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learnin…
Some recent trends in embeddings of time series and dynamic networks
Dag Tjøstheim, Martin Jullum, Anders Løland
We give a review of some recent developments in embeddings of time series and dynamic networks. We start out with traditional principal components and then look at extensions to dy…
Exabel's Factor Model
Øyvind Grotmol, Michael Scheuerer, Kjersti Aas +1
Factor models have become a common and valued tool for understanding the risks associated with an investing strategy. In this report we describe Exabel's factor model, we quantify…
Performance evaluation of volatility estimation methods for Exabel
Øyvind Grotmol, Martin Jullum, Kjersti Aas +1
Quantifying both historic and future volatility is key in portfolio risk management. This note presents and compares estimation strategies for volatility estimation in an estimatio…
Using Shapley Values and Variational Autoencoders to Explain Predictive Models with Dependent Mixed Features
Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum +1
Shapley values are today extensively used as a model-agnostic explanation framework to explain complex predictive machine learning models. Shapley values have desirable theoretical…
MCCE: Monte Carlo sampling of realistic counterfactual explanations
Annabelle Redelmeier, Martin Jullum, Kjersti Aas +1
We introduce MCCE: Monte Carlo sampling of valid and realistic Counterfactual Explanations for tabular data, a novel counterfactual explanation method that generates on-manifold, a…
groupShapley: Efficient prediction explanation with Shapley values for feature groups
Martin Jullum, Annabelle Redelmeier, Kjersti Aas
Shapley values has established itself as one of the most appropriate and theoretically sound frameworks for explaining predictions from complex machine learning models. The popular…
Statistical embedding: Beyond principal components
Dag Tjøstheim, Martin Jullum, Anders Løland
There has been an intense recent activity in embedding of very high dimensional and nonlinear data structures, much of it in the data science and machine learning literature. We su…
Explaining predictive models using Shapley values and non-parametric vine copulas
Kjersti Aas, Thomas Nagler, Martin Jullum +1
The original development of Shapley values for prediction explanation relied on the assumption that the features being described were independent. If the features in reality are de…
Explaining predictive models with mixed features using Shapley values and conditional inference trees
Annabelle Redelmeier, Martin Jullum, Kjersti Aas
It is becoming increasingly important to explain complex, black-box machine learning models. Although there is an expanding literature on this topic, Shapley values stand out as a…
Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Kjersti Aas, Martin Jullum, Anders Løland
Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from a complex machine learning model by…
Estimating seal pup production in the Greenland Sea using Bayesian hierarchical modeling
Martin Jullum, Thordis Thorarinsdottir, Fabian E. Bachl
The Greenland Sea is an important breeding ground for harp and hooded seals. Estimates of the annual seal pup production are critical factors in the abundance estimation needed for…