Results 41 to 50 of about 1,556,418 (219)
Learning Universe Model for Partial Matching Networks over Multiple Graphs [PDF]
arXiv, 2022 We consider the general setting for partial matching of two or multiple graphs, in the sense that not necessarily all the nodes in one graph can find their correspondences in another graph and vice versa. We take a universe matching perspective to this ubiquitous problem, whereby each node is either matched into an anchor in a virtual universe graph or
arxiv
The Matching Number and Hamiltonicity of Graphs [PDF]
arXiv, 2020 The matching number of a graph G is the size of a maximum matching in the graph. In this note, we present a sufficient condition involving the matching number for the Hamiltonicity of graphs.
arxiv
Popular Matchings with One-Sided Bias [PDF]
arXiv, 2022 Let $G = (A \cup B,E)$ be a bipartite graph where the set $A$ consists of agents or main players and the set $B$ consists of jobs or secondary players. Every vertex has a strict ranking of its neighbors. A matching $M$ is popular if for any matching $N$, the number of vertices that prefer $M$ to $N$ is at least the number that prefer $N$ to $M ...
arxiv
Pharmaceutical statistics, 2010 In a study comparing the effects of two treatments, the propensity score is the probability of assignment to one treatment conditional on a subject's measured baseline covariates.
Peter C Austin
semanticscholar +1 more source
Maximum Matchings and Popularity [PDF]
arXiv, 2020 Let $G$ be a bipartite graph where every node has a strict ranking of its neighbors. For every node, its preferences over neighbors extend naturally to preferences over matchings. Matching $N$ is more popular than matching $M$ if the number of nodes that prefer $N$ to $M$ is more than the number that prefer $M$ to $N$.
arxiv
Why Propensity Scores Should Not Be Used for Matching
Political Analysis, 2019 We show that propensity score matching (PSM), an enormously popular method of preprocessing data for causal inference, often accomplishes the opposite of its intended goal—thus increasing imbalance, inefficiency, model dependence, and bias.
Gary King, Richard A. Nielsen
semanticscholar +1 more source
GA-Net: Guided Aggregation Net for End-To-End Stereo Matching [PDF]
Computer Vision and Pattern Recognition, 2019 In the stereo matching task, matching cost aggregation is crucial in both traditional methods and deep neural network models in order to accurately estimate disparities. We propose two novel neural net layers, aimed at capturing local and the whole-image
Feihu Zhang +3 more
semanticscholar +1 more source
Propensity Score-Matching Methods for Nonexperimental Causal Studies
Review of Economics and Statistics, 2002 This paper considers causal inference and sample selection bias in nonexperimental settings in which (i) few units in the nonexperimental comparison group are comparable to the treatment units, and (ii) selecting a subset of comparison units similar to ...
Rajeev Dehejia, S. Wahba
semanticscholar +1 more source
S-Match: an Algorithm and an Implementation of Semantic Matching [PDF]
, 2004 We think of Match as an operator which takes two graph-like structures and produces a mapping between those nodes of the two graphs that correspond semantically to each other. Semantic matching is a novel approach where semantic correspondences are discovered by computing and returning as a result, the semantic information implicitly or explicitly ...
Giunchiglia, Fausto +2 more
openaire +5 more sources
Pattern Matching in Trees and Strings [PDF]
arXiv, 2007 We study the design of efficient algorithms for combinatorial pattern matching. More concretely, we study algorithms for tree matching, string matching, and string matching in compressed texts.
arxiv