StoqMa(k) equals StoqMa for any polynomial k via a positive value-based de Finetti theorem that approximates nonnegative product values with symmetric extensions.
Granha Jeronimo and P
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
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UNVERDICTED 5representative citing papers
Introduces a heavy-edge technique yielding a 1.622k-approximation for n-pairs shortest paths in weighted graphs, better than previous (2k-3) results.
Achieves (2k/3)-approximation for girth in weighted graphs in Õ(m + n^{1+2/k}) time for every k≥2, improving prior partial results, plus new fine-grained lower bounds for unweighted girth approximation.
Vertex-Coloring {0,1}-Edge-Weighting is W[1]-hard parameterized by feedback vertex set size, FPT by vertex cover size (with a restriction for the pre-weighted variant), and admits XP algorithms parameterized by treewidth.
A new data-synthesized instrumental variable estimator achieves finite-sample Lp consistency with sqrt(n) rate for linear-in-parameters models in discrete and continuous time, cutting bias by hundreds of times on Lorenz examples.
citing papers explorer
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The Collapse of Unentangled Stoquastic Merlin-Arthur Proof Systems
StoqMa(k) equals StoqMa for any polynomial k via a positive value-based de Finetti theorem that approximates nonnegative product values with symmetric extensions.
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Improved Approximation Algorithms for n-Pairs Shortest Paths
Introduces a heavy-edge technique yielding a 1.622k-approximation for n-pairs shortest paths in weighted graphs, better than previous (2k-3) results.
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Tighter bounds for weighted and unweighted shortest cycle approximation
Achieves (2k/3)-approximation for girth in weighted graphs in Õ(m + n^{1+2/k}) time for every k≥2, improving prior partial results, plus new fine-grained lower bounds for unweighted girth approximation.
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The Parameterized Complexity of Vertex-Coloring Edge-Weighting
Vertex-Coloring {0,1}-Edge-Weighting is W[1]-hard parameterized by feedback vertex set size, FPT by vertex cover size (with a restriction for the pre-weighted variant), and admits XP algorithms parameterized by treewidth.
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Instrumental variables system identification with $L^p$ consistency
A new data-synthesized instrumental variable estimator achieves finite-sample Lp consistency with sqrt(n) rate for linear-in-parameters models in discrete and continuous time, cutting bias by hundreds of times on Lorenz examples.