Christos Dimitrakakis

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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 ...

Intrusion detection is frequently used as a second line of defense in Mobile Ad-hoc Networks (MANETs). In this paper we examine how to properly use classification methods in intrusion detection for MANETs. In order to do so we evaluate five supervised clas ...

This paper proposes a simple linear Bayesian approach to reinforcement learning. We show that with an appropriate basis, a Bayesian linear Gaussian model is sufficient for accurately estimating the system dynamics, and in particular when we allow for corre ...

This thesis explores the application of ensemble methods to sequential learning tasks. The focus is on the development and the critical examination of new methods or novel applications of existing methods, with emphasis on supervised and reinforcement lear ...

There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most problems of interest, the optimal solution involves planning in an infinite ...

We propose a distributed upper confidence bound approach, DUCT, for solving distributed constraint optimization problems. We compare four variants of this approach with a baseline random sampling algorithm, as well as other complete and incomplete algorith ...

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strength ...

User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this p ...

The proliferation of online news creates a need for filtering interesting articles. Compared to other products, however, recommending news has specific challenges: news preferences are subject to trends, users do not want to see multiple articles with simi ...

The distribution of synaptic efficacies in neocortex has an approximately lognormal shape. Many weak synaptic connections coexist with few very strong connections such that only 20% of synapses contribute 50% of total synaptic strength. Furthermore, recent ...

We derive bounds on the expected loss for authenti-cation protocols in channels which are constrained due to noisyconditions and communication costs. This is motivated by anumber of authentication protocols, where at least some partof the authentication is ...

We generalise the problem of inverse reinforcement learning to multiple tasks, from multiple demonstrations. Each one may represent one expert trying to solve a different task, or as different experts trying to solve the same task. Our main contribution is ...

We present a class of models that, via a simple construction, enables exact, incremental, non-parametric, polynomial-time, Bayesian inference of conditional measures. The approach relies upon creating a sequence of covers on the conditioning variable and m ...

Christos Dimitrakakis

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Probabilistic inverse reinforcement learning in unknown environments

Intrusion Detection in MANET Using Classification Algorithms: The Effects of Cost and Model Selection

Linear Bayesian Reinforcement Learning

Ensembles for sequence learning

Complexity of stochastic branch and bound methods for belief tree search in Bayesian reinforcement learning

DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems

Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex

Statistical Decision Making for Authentication and Intrusion Detection

Personalized News Recommendation with Context Trees

Network Self-organization explains the distribution of synaptic efficacies in neocortex

Complexity of stochastic branch and bound methods for belief tree search in Bayesian reinforcement learning

Expected loss bounds for authentication in constrained channels

Bayesian multitask inverse reinforcement learning

Context models on sequences of covers

Guest Editors' Introduction: Special Section on Learning, Games and Security