Generalization Guarranties
Both PAC-Bayesian and Sample Compress learning frameworks have been shown instrumental for deriving tight (non-vacuous) generalization …
The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the …
Reconstruction functions are pivotal in sample compression theory, a framework for deriving tight generalization bounds. From a small …
The sample compression theory provides generalization guarantees for predictors that can be fully defined using a subset of the …
In statistical learning theory, a generalization bound usually involves a complexity measure imposed by the considered theoretical …
Embodied agents face significant challenges when tasked with performing actions in diverse environments, particularly in generalizing …
We study the generalisation properties of majority voting on finite ensembles of classifiers, proving margin-based generalisation …
The ALFRED environment features an embodied agent following instructions and accomplishing tasks in simulated home environments. …
A popular approach for sequential decision-making is to perform simulator-based search
guided with Machine Learning (ML) methods like …
A popular approach for sequential decision-making is to perform simulator-based search
guided with Machine Learning (ML) methods like …