216 citations · 394 across the 33 of their papers we have counts for
4 papers · 1 filter
Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm
Gholamali Aminian, Yuheng Bu, Laura Toni +2
Various approaches have been developed to upper bound the generalization error of a supervised learning algorithm. However, existing bounds are often loose and even vacuous when ev…
Learning Algorithm Generalization Error Bounds via Auxiliary Distributions
Gholamali Aminian, Saeed Masiha, Laura Toni +1
Generalization error bounds are essential for comprehending how well machine learning models work. In this work, we suggest a novel method, i.e., the Auxiliary Distribution Method,…
Semi-supervised Batch Learning From Logged Data
Gholamali Aminian, Armin Behnamnia, Roberto Vega +5
Off-policy learning methods are intended to learn a policy from logged data, which includes context, action, and feedback (cost or reward) for each sample point. In this work, we b…
An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift
Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili +2
A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution. However, this assumption is not satisfied in man…