A graph-manipulation technique reduces the cost of diagonal-preconditioned RHMC fixed-point iterations from quadratic to linear in dimension for coordinate-friendly targets.
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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Algorithm uses neural network verification to compute arbitrarily tight bounds on exact SHAP values for neural networks, recovering the exact values and scaling to larger feature spaces than prior exact methods.
Introduces decision-alignment to evaluate uncertainty metrics against downstream decision utilities and proposes prior-weighted proper scoring rules that align better in benchmarks and case studies.
Gini MDS replaces Euclidean distance in multidimensional scaling with a rank-and-value-based pseudo-distance controlled by a hyperparameter, claimed to yield more robust embeddings on noisy data than standard MDS.
citing papers explorer
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Verified SHAP: Provable Bounds for Exact Shapley Values of Neural Networks
Algorithm uses neural network verification to compute arbitrarily tight bounds on exact SHAP values for neural networks, recovering the exact values and scaling to larger feature spaces than prior exact methods.
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Decision-Aligned Evaluation of Uncertainty Quantification
Introduces decision-alignment to evaluate uncertainty metrics against downstream decision utilities and proposes prior-weighted proper scoring rules that align better in benchmarks and case studies.
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Optimizing Multidimensional Scaling in Gini Metric Spaces
Gini MDS replaces Euclidean distance in multidimensional scaling with a rank-and-value-based pseudo-distance controlled by a hyperparameter, claimed to yield more robust embeddings on noisy data than standard MDS.