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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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.
Introduces bAdag, an AdaGrad-based block coordinate gradient method with ergodic sublinear convergence proofs for smooth nonconvex objectives under block Lipschitz gradient assumptions, covering cyclic, uniform random, and Gauss-Southwell selection plus box constraints.
LoH adds a learnable choice operator to propositional logic, compiles formulas to differentiable graphs via fuzzy logic, subsumes prior NeSy models, and supports discretization to Boolean functions via the Gödel trick.
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bAdag: an adaptive block coordinate gradient method for smooth nonconvex functions
Introduces bAdag, an AdaGrad-based block coordinate gradient method with ergodic sublinear convergence proofs for smooth nonconvex objectives under block Lipschitz gradient assumptions, covering cyclic, uniform random, and Gauss-Southwell selection plus box constraints.