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Distributed Learning over Networks under Subspace Constraints

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Probabilistic inverse reinforcement learning in unknown environments

Christos Dimitrakakis

We consider the problem of learning by demonstration from agents acting in unknown stochastic Markov environments or games. Our aim is to estimate agent preferences in order to construct improved policies for the same task that the agents are trying to sol ...

2013

Evolution of division of labor in artificial societies

Pawel Lichocki

Natural and artificial societies often divide the workload between specialized members. For example, an ant worker may preferentially perform one of many tasks such as brood rearing, foraging and nest maintenance. A robot from a rescue team may specialize ...

EPFL2013

Modelling and Optimizing Mathematics Learning in Children

This study introduces a student model and control algorithm, optimizing mathematics learning in children. The adaptive system is integrated into a computer-based training system for enhancing numerical cognition aimed at children with developmental dyscalc ...

2013

On distributed online classification in the midst of concept drifts

Ali H. Sayed

In this work, we analyze the generalization ability of distributed online learning algorithms under stationary and non-stationary environments. We derive bounds for the excess-risk attained by each node in a connected network of learners and study the perf ...

Elsevier2013

Coordination and Sampling in Distributed Constraint Optimization

Brammert Ottens

The Distributed Constraint Optimization (DCOP) framework can be used to model a wide range of optimization problems that are inherently distributed. A distributed optimization problem can be viewed as a problem distributed over a set of agents, where agent ...

EPFL2012

Learning over social networks via diffusion adaptation

Ali H. Sayed

We propose a diffusion strategy to enable social learning over networks. Individual agents observe signals influenced by the state of the environment. The individual measurements are not sufficient to enable the agents to detect the true state of the envir ...

IEEE2012

Information Processing and Structure of Dynamical Networks

Ali Ajdari Rad

Networks are everywhere and we are confronted with many networks in our daily life. Networks such as Internet, World Wide Web, social, biological and economical networks have been subject to extensive studies in the last decade. The volume of publications ...

EPFL2011

Definitive Consensus for Distributed Data Inference

Leonidas Georgopoulos

Inference from data is of key importance in many applications of informatics. The current trend in performing such a task of inference from data is to utilise machine learning algorithms. Moreover, in many applications that it is either required or is pref ...

EPFL2011

Analysis of Spatial and Incremental LMS Processing for Distributed Estimation

Ali H. Sayed

Consider a set of nodes distributed spatially over some region forming a network, where every node takes measurements of an underlying process. The objective is for every node in the network to estimate some parameter of interest from these measurements by ...

IEEE2011

Mobile Adaptive Networks

Ali H. Sayed

In this paper, we investigate the self-organization and cognitive abilities of adaptive networks when the individual agents are allowed to move in pursuit of a target. The nodes act as adaptive entities with localized processing and are able to respond to ...

IEEE2011