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Yuheng Bu

31 papers hereh-index 16864 citations45 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author10
  • middle author17
  • last author4

Across the 31 of 31 papers where every author was matched, so the position is known.

fields
  • cs.LG17
  • cs.IT8
  • eess.SP3
  • stat.ML2
  • cs.CV1
same name
  • Yuheng Bu — 22 papers, h 7
  • Yuheng Bu — 6 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152026
most citedTightening Mutual Information Based Bounds on Generalization Error

78 citations · 130 across the 30 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.IT2024

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…

cs.IT2024

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…

cs.LG2024★ 1 cited

Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?

Maohao Shen, J. Jon Ryu, Soumya Ghosh +4

This paper questions the effectiveness of a modern predictive uncertainty quantification approach, called \emph{evidential deep learning} (EDL), in which a single neural network mo…

cs.LG2024

Operator SVD with Neural Networks via Nested Low-Rank Approximation

J. Jon Ryu, Xiangxiang Xu, H. S. Melihcan Erol +3

Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading eigenvalues and eigenfunctions, is a fundamental task in many machine learning and scien…

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