9 citations · 32 across the 21 of their papers we have counts for
7 papers · 1 filter
Information-theoretic Analysis of the Gibbs Algorithm: An Individual Sample Approach
Youheng Zhu, Yuheng Bu
Recent progress has shown that the generalization error of the Gibbs algorithm can be exactly characterized using the symmetrized KL information between the learned hypothesis and…
An Algorithm for Computing the Capacity of Symmetrized KL Information for Discrete Channels
Haobo Chen, Gholamali Aminian, Yuheng Bu
Symmetrized Kullback-Leibler (KL) information (\(I_{\mathrm{SKL}}\)), which symmetrizes the traditional mutual information by integrating Lautum information, has been shown as a cr…
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…
How Does Pseudo-Labeling Affect the Generalization Error of the Semi-Supervised Gibbs Algorithm?
Haiyun He, Gholamali Aminian, Yuheng Bu +2
We provide an exact characterization of the expected generalization error (gen-error) for semi-supervised learning (SSL) with pseudo-labeling via the Gibbs algorithm. The gen-error…
Tighter Expected Generalization Error Bounds via Convexity of Information Measures
Gholamali Aminian, Yuheng Bu, Gregory Wornell +1
Generalization error bounds are essential to understanding machine learning algorithms. This paper presents novel expected generalization error upper bounds based on the average jo…
Linear-Complexity Exponentially-Consistent Tests for Universal Outlying Sequence Detection
Yuheng Bu, Shaofeng Zou, Venugopal V. Veeravalli
The problem of universal outlying sequence detection is studied, where the goal is to detect outlying sequences among sequences of samples. A sequence is considered as outlying…