arXiv:2511. 18945v4 Announce Type: replace Abstract: We propose a fully data-driven approach to designing mutual information (MI) estimators.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.
arXiv:2606. 25188v1 Announce Type: new Abstract: Efficient uncertainty quantification (UQ) is essential for trustworthy large-scale learning.
arXiv:2606. 00241v1 Announce Type: cross Abstract: Measuring statistical dependency between high-dimensional random variables is a fundamental task in data science and machine learning.
arXiv:2605. 04847v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) in graph neural networks (GNNs) is crucial in high-stakes domains but remains a significant challenge.
arXiv:2607. 12730v1 Announce Type: cross Abstract: Smart-building load forecasters are often trained offline on dense, multivariate, high-frequency data, but deployment may provide only hourly, feature-limited inputs.
arXiv:2209. 01754v5 Announce Type: replace-cross Abstract: The empirical risk minimization approach to data-driven decision making requires access to training data drawn under the same conditions as those that will be faced when the decision rule is deployed.
arXiv:2607. 27710v1 Announce Type: new Abstract: Mutual information is a general measure of statistical dependence that captures both linear and nonlinear relationships between random variables.