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cs.LG2026
On the Properties of Feature Attribution for Supervised Contrastive Learning
Leonardo Arrighi, Julia Eva Belloni, Aurélie Gallet +3
Most Neural Networks (NNs) for classification are trained using Cross-Entropy as a loss function. This approach requires the model to have an explicit classification layer. However…
cs.LG2026
Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
Matheus Camilo da Silva, Leonardo Arrighi, Ana Carolina Lorena +1
AutoClustering methods aim to automate unsupervised learning tasks, including algorithm selection (AS), hyperparameter optimization (HPO), and pipeline synthesis (PS), by often lev…