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Social Learning Under Inferential Attacks

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Validation of probabilistic classifiers

Michel Bierlaire, Timothy Michael Hillel

Non-parametric probabilistic classification models are increasingly being investigated as an alternative to Discrete Choice Models (DCMs), e.g. for predicting mode choice. There exist many strategies within the literature for model selection between DCMs, ...

2018

Risk Perception and Epidemics in Complex Computer Networks

Emanuele Massaro

We present a self-organised method for quickly obtaining the epidemic threshold of infective processes on networks. Starting from simple percolation models, we introduce the possibility that the effective infection probability is affected by the perception ...

IEEE2018

Tomography Of Adaptive Multi-Agent Networks Under Limited Observation

Ali H. Sayed

This work studies the problem of inferring from streaming data whether an agent is directly influenced by another agent over an adaptive network of interacting agents. Agent i influences agent j if they are connected, and if agent j uses the information fr ...

IEEE2018

The detection of two-component mixture alternatives

Daria Rukina

In this thesis, we deal with one of the facets of the statistical detection problem. We study a particular type of alternative, the mixture model. We consider testing where the null hypothesis corresponds to the absence of a signal, represented by some kno ...

EPFL2018

Horizontal Metropolis: a research and a design hypothesis

Martina Barcelloni Corte

Lecture (for the course Urbanisme et Territoires) ...

2018

Mapping large organizations

Dario Rodighiero

Today organizations are more than ever complex systems. They are large, ramified, distributed, and intertwined so that their organic structure seems like a tangle of activities. Day by day individuals contribute to keep these structures alive with their wo ...

2017

Bayesian Inference For The Brown-Resnick Process, With An Application To Extreme Low Temperatures

Anthony Christopher Davison, Emeric Rolland Georges Thibaud

The Brown-Resnick max-stable process has proven to be well suited for modeling extremes of complex environmental processes, but in many applications its likelihood function is intractable and inference must be based on a composite likelihood, thereby preve ...

Inst Mathematical Statistics2016

On the Learning Behavior of Adaptive Networks - Part II: Performance Analysis

Ali H. Sayed

Part I of this paper examined the mean-square stability and convergence of the learning process of distributed strategies over graphs. The results identified conditions on the network topology, utilities, and data in order to ensure stability; the results ...

IEEE2015

Connection-type-specific biases make uniform random network models consistent with cortical recordings

Wulfram Gerstner, Carl Petersen, Tim Vogels, Michael Avermann, Christian Tomm

Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a stu ...

American Physiological Society2014

, , ,

The objective of this paper was to perform new analyses about the structure and evolution of the Incanto system. The hypothesis was to go beyond the textual narrative or even cartographic representation thanks to network analysis, which could potentially o ...

2014