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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2026 4verdicts
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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.
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Hessian-informed, Coordinate Friendly Hamiltonian Monte Carlo in Linear Time
A graph-manipulation technique reduces the cost of diagonal-preconditioned RHMC fixed-point iterations from quadratic to linear in dimension for coordinate-friendly targets.