Additive εn²-approximation for graph edit distance on VC-dimension-d graphs in n^{O(d/ε²)} time, with extensions to quadratic assignment problems and a Weisfeiler-Leman dimension bound for robust graph isomorphism.
Describing Graphs: A First-Order Approach to Graph Canonization
7 Pith papers cite this work. Polarity classification is still indexing.
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Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
Multi-modal BNN surrogates with conjugate last-layer SVI estimation improve prediction accuracy and uncertainty quantification over uni-modal baselines for scalar and time-series data with missing observations.
A consistent bias-corrected estimator based on blockwise top-two order statistics is developed for extreme value analysis after showing the naive independence-likelihood approach is inconsistent.
Agent-based simulations reveal that rigid specialist roles in ad-hoc multi-agent teams generate system bottlenecks, workload inequality, fragmented networks, and diminishing returns from added team members due to communication costs.
Shape optimization of Maxwell eigenvalues via adjoint sensitivities on a reference domain, solved with a damped inverse BFGS method and mixed finite elements.
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.
citing papers explorer
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Robust Graph Isomorphism, Quadratic Assignment and VC Dimension
Additive εn²-approximation for graph edit distance on VC-dimension-d graphs in n^{O(d/ε²)} time, with extensions to quadratic assignment problems and a Weisfeiler-Leman dimension bound for robust graph isomorphism.
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Fragmentation is Efficiently Learnable by Quantum Neural Networks
Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
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Multi-modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation
Multi-modal BNN surrogates with conjugate last-layer SVI estimation improve prediction accuracy and uncertainty quantification over uni-modal baselines for scalar and time-series data with missing observations.
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Extreme Value Analysis based on Blockwise Top-Two Order Statistics
A consistent bias-corrected estimator based on blockwise top-two order statistics is developed for extreme value analysis after showing the naive independence-likelihood approach is inconsistent.
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Too Many Specialists: Emergent Inefficiencies and Bottlenecks for Multi-agent Ad-hoc Collaboration
Agent-based simulations reveal that rigid specialist roles in ad-hoc multi-agent teams generate system bottlenecks, workload inequality, fragmented networks, and diminishing returns from added team members due to communication costs.
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Numerical Eigenvalue Optimization by Shape-Variations for Maxwell's Eigenvalue Problem
Shape optimization of Maxwell eigenvalues via adjoint sensitivities on a reference domain, solved with a damped inverse BFGS method and mixed finite elements.
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
Bayesian optimization automates the scientific discovery cycle by modeling observations with surrogate models and using acquisition functions to select experiments that balance known information with new exploration.