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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 asynchronous environment. At the heart of our new perspective stands… (More)

- Andrei Giurgiu, Nicolas Macris, Rüdiger L. Urbanke
- IEEE Transactions on Information Theory
- 2016

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)

- Eric W. Tramel, Santhosh Kumar, Andrei Giurgiu, Andrea Montanari
- ArXiv
- 2014

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)

- Andrei Giurgiu, Nicolas Macris, Rüdiger L. Urbanke
- ISIT
- 2012

- Andrei Giurgiu, Rachid Guerraoui, Kévin Huguenin, Anne-Marie Kermarrec
- Inf. Comput.
- 2010

This paper defines the problem of Scalable Secure Computing in a Social network: we call it the S 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)

- Andrei Giurgiu, Nicolas Macris, Rüdiger L. Urbanke
- 2013 IEEE International Symposium on Information…
- 2013

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)

- Andrei Giurgiu, Nicolas Macris, Rüdiger L. Urbanke
- ArXiv
- 2013

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)

- T Fekete, I Dimitrescu, E Sopon, A Giurgiu
- Orvosi hetilap
- 1968

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)