Local resampling and backtracking algorithms for the Lovász Local Lemma achieve near-linear total work in the number of adaptive updates when constraints are added or removed.
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New random-order semi-streaming algorithms improve approximation guarantees over adversarial-order results for submodular maximization under matroids and related constraints, with an exponential reduction in passes needed for near-optimal matroid results.
A kernel-copula embedding statistic equals zero exactly when causal dependence between X and Y is stable and is strictly positive otherwise, with a near-linear estimator and convergence rates provided.
citing papers explorer
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Dynamic Construction of the Lov\'asz Local Lemma
Local resampling and backtracking algorithms for the Lovász Local Lemma achieve near-linear total work in the number of adaptive updates when constraints are added or removed.
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Semi-Streaming Algorithms for Submodular Maximization under Random Arrival Order
New random-order semi-streaming algorithms improve approximation guarantees over adversarial-order results for submodular maximization under matroids and related constraints, with an exponential reduction in passes needed for near-optimal matroid results.
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Detecting Changes in Causal Dependence with Kernels and Copulas
A kernel-copula embedding statistic equals zero exactly when causal dependence between X and Y is stable and is strictly positive otherwise, with a near-linear estimator and convergence rates provided.