Andrei Giurgiu

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Most people believe that renaming is easy: simply choose a name at random; if more than one process selects the same name, then try again. We highlight the issues that occur when trying to implement such a scheme and shed new light on the read-write complexity of randomized renaming in an asyn-chronous environment. At the heart of our new perspective stands(More)
The aim of this paper is to show that spatial coupling can be viewed not only as a means to build better graphical models, but also as a tool to better understand uncoupled models. The starting point is the observation that some asymptotic properties of graphical models are easier to prove in the case of spatial coupling. In such cases, one can then use the(More)
This paper defines the problem of Scalable Secure Computing in a Social network: we call it the S 3 problem. In short, nodes, directly reflecting on associated users, need to compute a function f : V → U of their inputs in a set of constant size, in a scalable and secure way. Scalability means that the message and computational complexity of the distributed(More)
—The aim of this paper is to show that spatial coupling can be viewed not only as a means to build better graphical models, but also as a tool to better understand uncoupled models. The starting point is the observation that some asymptotic properties of graphical models are easier to prove in the case of spatial coupling. In such cases, one can then use(More)
The aim of this paper is to show that spatial coupling can be viewed not only as a means to build better graphical models, but also as a tool to better understand uncoupled models. The starting point is the observation that some asymptotic properties of graphical models are easier to prove in the case of spatial coupling. In such cases, one can then use the(More)
Investigations on spatially coupled codes have lead to the conjecture that, in the infinite size limit, the average input-output conditional entropy for spatially coupled low-density parity-check ensembles, over binary memoryless symmetric channels, equals the entropy of the underlying individual ensemble. We give a self-contained proof of this conjecture(More)
These notes review six lectures given by Prof. Andrea Montanari on the topic of statistical estimation for linear models. The first two lectures cover the principles of signal recovery from linear measurements in terms of minimax risk. Subsequent lectures demonstrate the application of these principles to several practical problems in science and(More)
Executive Summary Observable Operator Models (OOM) are statistical tools developed in the field of Machine Learning with the purpose of modelling certain classes of stochastic processes. They have been highly successful, both in terms of efficiency and accuracy of results, compared to the more widely-used Hidden Markov Models (HMM), when used on stationary(More)
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