Inference problems on graphs arise naturally when trying to make sense of network data. Oftentimes, these problems are formulated as intractable optimization programs. This renders the need for fast heuristics to find adequate solutions and for the study of their performance. For a certain class of problems, Javanmard et al. (1) successfully use tools from statistical physics to analyze the performance of semidefinite programming relaxations, an important heuristic for intractable problems.National Science Foundation (U.S.) (Grant DMS- 1317308
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International audienceWe present a simple and flexible method to prove consistency of semidefinite o...
Abstract. We present a simple and flexible method to prove consis-tency of semidefinite optimization...
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Resea...
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Graphs are a rich and fundamental object of study, of interest from both theoretical andapplied poin...
Graph learning is an inference problem of estimating connectivity of a graph from a collection of ep...
International audienceThe problem of predicting connections between a set of data points finds many ...
International audienceIn semi-supervised graph clustering setting, an expert provides cluster member...
The mixture of data in real-life exhibits structure or connection property in nature. Typical data i...
The problem of detecting communities in a graph is maybe one the most studied inference problems, gi...
Today witnesses an explosion of data coming from various types of networks such as online social net...
Many maximum likelihood estimation problems are known to be intractable in the worst case. A common ...
International audienceWe present a simple and flexible method to prove consistency of semidefinite o...
Abstract. We present a simple and flexible method to prove consis-tency of semidefinite optimization...
Abstract—The binary symmetric stochastic block model deals with a random graph of n vertices partiti...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Resea...
The stochastic block model is one of the oldest and most ubiquitous models for studying clustering a...
Probabilistic inference in Bayesian networks, and even reasoning within error bounds are known to be...
Graphical models are used to describe the interactions in structures, such as the nodes in decoding ...
Graphs are a rich and fundamental object of study, of interest from both theoretical andapplied poin...
Graph learning is an inference problem of estimating connectivity of a graph from a collection of ep...
International audienceThe problem of predicting connections between a set of data points finds many ...
International audienceIn semi-supervised graph clustering setting, an expert provides cluster member...
The mixture of data in real-life exhibits structure or connection property in nature. Typical data i...