1 citations · 1 across the 6 of their papers we have counts for
6 papers
Denoising Distances in Metric Measure Spaces
Han Huang, Pakawut Jiradilok, Elchanan Mossel
Recent work studied the problem of finding clusters and denoising pairwise distances from noisy distances of points sampled on a manifold. We study the same problems in more genera…
Denoising distances beyond the volumetric barrier
Han Huang, Pakawut Jiradilok, Elchanan Mossel
We study the problem of reconstructing the latent geometry of a -dimensional Riemannian manifold from a random geometric graph. While recent works have made significant progress…
Reconstructing Riemannian Metrics From Random Geometric Graphs
Han Huang, Pakawut Jiradilok, Elchanan Mossel
Random geometric graphs are random graph models defined on metric measure spaces. A random geometric graph is generated by first sampling points from a metric space and then connec…
Optimal Low degree hardness for Broadcasting on Trees
Han Huang, Elchanan Mossel
Broadcasting on trees is a fundamental model from statistical physics that plays an important role in information theory, noisy computation and phylogenetic reconstruction within c…
Low Degree Hardness for Broadcasting on Trees
Han Huang, Elchanan Mossel
We study the low-degree hardness of broadcasting on trees. Broadcasting on trees has been extensively studied in statistical physics, in computational biology in relation to phylog…
Reconstructing the Geometry of Random Geometric Graphs
Han Huang, Pakawut Jiradilok, Elchanan Mossel
Random geometric graphs are random graph models defined on metric spaces. Such a model is defined by first sampling points from a metric space and then connecting each pair of samp…