Blar i Vitenskapelige tidsskriftartikler og konferanseartikler med fagfellevurdering (NVI-kategori) på forfatter "Løland, Anders"
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Explaining individual predictions when features are dependent: More accurate approximations to Shapley values
Aas, Kjersti; Jullum, Martin; Løland, Anders (Journal article; Peer reviewed, 2021)Explaining complex or seemingly simple machine learning models is an important practical problem. We want to explain individual predictions from such models by learning simple, interpretable explanations. Shapley value is ... -
eXplego: An interactive Tool that Helps you Select Appropriate XAI-methods for your Explainability Needs
Jullum, Martin; Sjødin, Jacob; Prabhu, Robindra; Løland, Anders (Journal article; Peer reviewed, 2023)The growing demand for transparency, interpretability, and explainability of machine learning models and AI systems has fueled the development of methods aimed at understanding the properties and behavior of such models ... -
MCCE: Monte Carlo sampling of valid and realistic counterfactual explanations for tabular data
Redelmeier, Annabelle Alice; Jullum, Martin; Aas, Kjersti; Løland, Anders (Journal article; Peer reviewed, 2024)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 ... -
Some recent trends in embeddings of time series and dynamic networks
Tjøstheim, Dag Bjarne; Jullum, Martin; Løland, Anders (Journal article; Peer reviewed, 2023)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 dynamic factor models for time series. Unlike ... -
Some recent trends in embeddings of time series and dynamic networks
Tjøstheim, Dag Bjarne; Jullum, Martin; Løland, Anders (Journal article; Peer reviewed, 2023)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 dynamic factor models for time series. Unlike ... -
Statistical Embedding: Beyond Principal Components
Tjøstheim, Dag Bjarne; Jullum, Martin; Løland, Anders (Journal article; Peer reviewed, 2023)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 survey this activity in four parts. In the ... -
Statistical Embedding: Beyond Principal Components
Tjøstheim, Dag Bjarne; Jullum, Martin; Løland, Anders (Journal article; Peer reviewed, 2023)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 survey this activity in four parts. In the ...