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Found 4 papers, 1 papers with code
GREASE: Generate Factual and Counterfactual Explanations for GNN-based Recommendations
Although powerful, it is very difficult for a GNN-based recommender system to attach tangible explanations of why a specific item ends up in the list of suggestions for a given user.
ReLAX: Reinforcement Learning Agent eXplainer for Arbitrary Predictive Models
However, existing CF generation methods either exploit the internals of specific models or depend on each sample's neighborhood, thus they are hard to generalize for complex models and inefficient for large datasets.
Costly Features Classification using Monte Carlo Tree Search
We consider the problem of costly feature classification, where we sequentially select the subset of features to make a balance between the classification error and the feature cost.
Item Response Theory based Ensemble in Machine Learning
In this article, we propose a novel probabilistic framework to improve the accuracy of a weighted majority voting algorithm.
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