Parallel algorithm for matroid basis computation with O(n^{1/3} log^{1/3} n) round complexity, nearly matching the KUW lower bound.
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2026 4verdicts
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Protocol learns k-local Lindbladians to ε accuracy with Õ(n^{2k}/ε²) samples and projects to valid generators; improves to log n under sparsity assumptions.
Any n-qubit QC Hamiltonian sparsifies to Õ(n/ε²) terms preserving all state energies within 1±ε using invariant subspace decomposition and the Alon-Kozma operator inequality.
COPF is a decision-layer framework for deployment-stable counterfactual fairness in online link recommendation on evolving graphs, using exposure counterfactuals, propensity logging, residual outcome indistinguishability, and graph-aware doubly robust estimators, supported by a noisy transfer theore
citing papers explorer
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A Near-Optimal Parallel Algorithm for Finding Matroid Bases
Parallel algorithm for matroid basis computation with O(n^{1/3} log^{1/3} n) round complexity, nearly matching the KUW lower bound.
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Robust Structure Learning of $k$-local Lindbladians
Protocol learns k-local Lindbladians to ε accuracy with Õ(n^{2k}/ε²) samples and projects to valid generators; improves to log n under sparsity assumptions.
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Quantum Cut Sparsifiers
Any n-qubit QC Hamiltonian sparsifies to Õ(n/ε²) terms preserving all state energies within 1±ε using invariant subspace decomposition and the Alon-Kozma operator inequality.
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COPF: An Online Framework for Deployment-Stable Counterfactual Fairness in Evolving Graphs
COPF is a decision-layer framework for deployment-stable counterfactual fairness in online link recommendation on evolving graphs, using exposure counterfactuals, propensity logging, residual outcome indistinguishability, and graph-aware doubly robust estimators, supported by a noisy transfer theore