This is a challenging inference task given the need to reason beyond the local appearance of hands. The lack of training annotations indicating which object or parts of an object the hand is in contact with further complicates the task.

The Information Bottleneck (IB) objective uses information theory to formulate a task-performance versus robustness trade-off.

We study the generalisation properties of majority voting on finite ensembles of classifiers, proving margin-based generalisation bounds via the PAC-Bayes theory. These provide state-of-the-art guarantees on a number of classification tasks. Our central results leverage the Dirichlet posteriors studied recently by Zantedeschi et al. (2021) for training voting classifiers; in contrast to that work our bounds apply to non-randomised votes via the use of margins. Our contributions add perspective to the debate on the "margins theory" proposed by Schapire et al. (1998) for the generalisation of ensemble classifiers.

The widely studiedGeneralized Min-Sum-Set-Cover(GMSSC) problem serves as a formal model for the setting above. GMSSC is NP-hard and the standard application ofno-regretonline learning algorithms iscomputationally inefficient, because they operate in the space of rankings. In this work, we show how to achievelowregret for GMSSC inpolynomial-time.

Traditional solutions prioritizing easy or hard samples lack the flexibility to handle such a variety simultaneously. Recent work has proposed a more reasonable data selection principle by examining the data's impact on the model's generalization loss.