To design an efficient intervention, decision-makers need not only to estimate the total effect of the intervention, but also understand the underlying causal mechanisms driving this effect.

As a result, these and other large conferences must rely on automated systems to decide what members of the impaneled reviewer pool will review each paper.

Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment.

However, there is a noticeable gap in analysis for multiclass classification, with only a handful of results which themselves are restricted to the cross-entropy loss.

As hyper-parameters are ubiquitous and can significantly affect the model performance, hyper-parameter optimization is extremely important in machine learning.

While several previous works have focused on classifying close-set samples and detecting open-set samples during testing, it's still essential to be able to classify unknown subjects as human beings.

Several recent works have attempted to deal with the above issues, which concentrate on the following two aspects.