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Found 7 papers, 2 papers with code
Explainable Adaptive Tree-based Model Selection for Time Series Forecasting
In this context, we propose a novel method for the online selection of tree-based models using the TreeSHAP explainability method in the task of time series forecasting.
An Empirical Evaluation of the Rashomon Effect in Explainable Machine Learning
The Rashomon Effect describes the following phenomenon: for a given dataset there may exist many models with equally good performance but with different solution strategies.
Energy Efficiency Considerations for Popular AI Benchmarks
Advances in artificial intelligence need to become more resource-aware and sustainable.
Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning
The resulting attributions can be interpreted as explanations for why a specific circuit works well for a given task, improving the understanding of how to construct parameterized (or variational) quantum circuits, and fostering their human interpretability in general.
Explainable Artificial Intelligence (XAI)
Quantum Machine Learning
Shapley Values with Uncertain Value Functions
We propose a novel definition of Shapley values with uncertain value functions based on first principles using probability theory.
Yes We Care! -- Certification for Machine Learning Methods through the Care Label Framework
How can they be guaranteed for a certain implementation of a machine learning model?
Explainable Machine Learning with Prior Knowledge: An Overview
This survey presents an overview of integrating prior knowledge into machine learning systems in order to improve explainability.
