7 citations · 32 across the 11 of their papers we have counts for
20 papers
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…
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…
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm
Yuheng Bu, Gholamali Aminian, Laura Toni +2
We provide an information-theoretic analysis of the generalization ability of Gibbs-based transfer learning algorithms by focusing on two popular transfer learning approaches, -…
Characterizing the Generalization Error of Gibbs Algorithm with Symmetrized KL information
Gholamali Aminian, Yuheng Bu, Laura Toni +2
Bounding the generalization error of a supervised learning algorithm is one of the most important problems in learning theory, and various approaches have been developed. However,…
Spatio-temporal Graph-RNN for Point Cloud Prediction
Pedro Gomes, Silvia Rossi, Laura Toni
In this paper, we propose an end-to-end learning network to predict future frames in a point cloud sequence. As main novelty, an initial layer learns topological information of poi…
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms
Gholamali Aminian, Laura Toni, Miguel R. D. Rodrigues
Generalization error bounds are critical to understanding the performance of machine learning models. In this work, building upon a new bound of the expected value of an arbitrary…