most citedSAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

5 citations · 7 across the 8 of their papers we have counts for

collaborators

8 papers

cs.LG20245 cited

SAUC: Sparsity-Aware Uncertainty Calibration for Spatiotemporal Prediction with Graph Neural Networks

Dingyi Zhuang, Yuheng Bu, Guang Wang +2

Quantifying uncertainty is crucial for robust and reliable predictions. However, existing spatiotemporal deep learning mostly focuses on deterministic prediction, overlooking the i…

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

Class-wise Generalization Error: an Information-Theoretic Analysis

Firas Laakom, Yuheng Bu, Moncef Gabbouj

Existing generalization theories of supervised learning typically take a holistic approach and provide bounds for the expected generalization over the whole data distribution, whic…

cs.CV2023

Feature Learning in Image Hierarchies using Functional Maximal Correlation

Bo Hu, Yuheng Bu, José C. Príncipe

This paper proposes the Hierarchical Functional Maximal Correlation Algorithm (HFMCA), a hierarchical methodology that characterizes dependencies across two hierarchical levels in…

eess.SP2023

A Bilateral Bound on the Mean-Square Error for Estimation in Model Mismatch

Amir Weiss, Alejandro Lancho, Yuheng Bu +1

A bilateral (i.e., upper and lower) bound on the mean-square error under a general model mismatch is developed. The bound, which is derived from the variational representation of t…

cs.LG2023

Reliable Gradient-free and Likelihood-free Prompt Tuning

Maohao Shen, Soumya Ghosh, Prasanna Sattigeri +3

Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs. Fine-tuning such models to downstream tasks is challenging b…