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Cauchy-Schwarz Divergence Information Bottleneck for Regression
April 30, 2024, 4:42 a.m. | Shujian Yu, Xi Yu, Sigurd L{\o}kse, Robert Jenssen, Jose C. Principe
cs.LG updates on arXiv.org arxiv.org
Abstract: The information bottleneck (IB) approach is popular to improve the generalization, robustness and explainability of deep neural networks. Essentially, it aims to find a minimum sufficient representation $\mathbf{t}$ by striking a trade-off between a compression term $I(\mathbf{x};\mathbf{t})$ and a prediction term $I(y;\mathbf{t})$, where $I(\cdot;\cdot)$ refers to the mutual information (MI). MI is for the IB for the most part expressed in terms of the Kullback-Leibler (KL) divergence, which in the regression case corresponds to prediction …
arxiv cs.it cs.lg divergence information math.it regression stat.ml type
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