Christos Dimitrakakis

2021

SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning

A novel individually rational objective in multi-agent multi-armed bandits: Algorithms and regret bounds

Epistemic Risk-Sensitive Reinforcement Learning

Bayesian Reinforcement Learning via Deep, Sparse Sampling

2020

Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning

Bayesian Fairness

2019

Epistemic Risk-Sensitive Reinforcement Learning

Bayesian optimization in ab initio nuclear physics

Privacy of Real-Time Pricing in Smart Grid

VIVO: A secure, privacy-preserving, and real-time crowd-sensing framework for the Internet of Things

DUCT: An upper confidence bound approach to distributed constraint optimization problems

A Differentially Private Encryption Scheme

Adaptive Sampling-based View Planning under Time Constraints

Multi-view decision processes: The helper-AI problem

Near-Optimal Blacklisting

Thompson Sampling for Stochastic Bandits with Graph Feedback

Achieving Privacy in the Adversarial Multi-Armed Bandit

Bayesian Inference for Least Squares Temporal Difference Regularization

Differential privacy for Bayesian inference through posterior sampling

On the differential privacy of Bayesian inference

Optimal advertisement strategies for small and big companies

Synergistic user ↔ context analytics

Algorithms for Differentially Private Multi-Armed Bandits

Foreword - Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security

Expected loss analysis for authentication in constrained channels

Differentially private, multi-agent multi-armed bandits

Distance-Bounding Protocols: Are You Close Enough?

On the Leakage of Information in Biometric Authentication

Generalised entropy MDPs and Minimax Regret

2014

Cover Tree Bayesian Reinforcement Learning

Robust and private Bayesian inference

Differential Privacy and Private Bayesian Inference.

Foreword - Proceedings of the ACM Conference on Computer and Communications Security

The reinforcement learning competition

Usable ABC reinforcement learning

Proceedings of the ACM Conference on Computer and Communications Security: Foreword

Intrusion detection in MANET using classification algorithms: The effects of cost and model selection

Summary/overview for artificial intelligence and security (AISec'13)

Generalization and Interference in Human Motor Control

2013

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

ABC Reinforcement Learning

Personalized news recommendation with context trees

2013

Linear Bayesian reinforcement learning

2013

Monte-Carlo utility estimates for Bayesian reinforcement learning

2013

On selecting the nonce length in distance bounding protocols

2013

Probabilistic inverse reinforcement learning in unknown environments

Near-optimal Node Blacklisting in Adversarial Networks

Guest editors' introduction: Special section on learning, games, and security

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

Expected loss bounds for authentication in constrained channels

Sparse reward processes

Robust Bayesian reinforcement learning through tight lower bounds

Preference elicitation and inverse reinforcement learning

Network Self-organization Explains the Distribution of Synaptic Efficacies in Neocortex

Bayesian multitask inverse reinforcement learning

Phoneme and sentence-level ensembles for speech recognition

Context model inference for large or partially observable MDPs

Reid et al.'s distance bounding protocol and mafia fraud attacks over noisy channels

Proceedings of the 1st International ECML/PKDD Workshop on Privacy and Security Issues in Data Mining and Machine Learning (PSDML 2010)

Bayesian variable order Markov models

Efficient methods for near-optimal sequential decision making under uncertainty

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

Intrusion detection using cost-sensitive classification

Statistical decision making for authentication and intrusion detection

Cost-minimising strategies for data labelling: Optimal stopping and active learning

Algorithms and Bounds for Sampling-based Approximate Policy Iteration

Exploration in POMDPs

Rollout sampling approximate policy iteration

Tree exploration for bayesian RL exploration

Nearly Optimal Exploration-Exploitation Decision Thresholds

Ensembles for sequence learning

Online adaptive policies for ensemble classifiers

Gradient-based Estimates of Return Distributions

Boosting word error rates

Boosting HMMs with an application to speech recognition