Christos Dimitrakakis

Approximate Inference for the Bayesian Fairness Framework

2023

Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer

Minimax-Bayes Reinforcement Learning

2023

Minimax-Bayes Reinforcement Learning

Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty

Interactive Inverse Reinforcement Learning for Cooperative Games

2022

SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning

Bayesian Reinforcement Learning via Deep, Sparse Sampling

2020

Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning

2020

Epistemic risk-sensitive reinforcement learning

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

2019

Bayesian optimization in ab initio nuclear physics

Bayesian Fairness

Privacy of Real-Time Pricing in Smart Grid

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

Multi-view decision processes: The helper-AI problem

Differential privacy for Bayesian inference through posterior sampling

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

Adaptive Sampling-based View Planning under Time Constraints

A Differentially Private Encryption Scheme

Thompson Sampling for Stochastic Bandits with Graph Feedback

Achieving Privacy in the Adversarial Multi-Armed Bandit

Bayesian Inference for Least Squares Temporal Difference Regularization

Near-Optimal Blacklisting

Algorithms for Differentially Private Multi-Armed Bandits

Optimal advertisement strategies for small and big companies

On the differential privacy of Bayesian inference

2016

Synergistic user ↔ context analytics

Distance-Bounding Protocols: Are You Close Enough?

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

On the Leakage of Information in Biometric Authentication

Generalised entropy MDPs and Minimax Regret

Robust and private Bayesian inference

Workshop Summary of AiSec'14-2014 Workshop on Artificial Intelligent and Security

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

The reinforcement learning competition

Usable ABC reinforcement learning

Differential Privacy and Private Bayesian Inference.

2014

Cover Tree Bayesian Reinforcement Learning

ABC Reinforcement Learning

2013

Linear Bayesian reinforcement learning

Personalized news recommendation with context trees

2013

Probabilistic inverse reinforcement learning in unknown environments

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

2013

Monte-Carlo utility estimates for Bayesian reinforcement learning

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

2013

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

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

2013

On selecting the nonce length in distance bounding protocols

Generalization and Interference in Human Motor Control

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

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

Near-optimal Node Blacklisting in Adversarial Networks

Sparse reward processes

Expected loss bounds for authentication in constrained channels

Robust Bayesian reinforcement learning through tight lower bounds

Phoneme and sentence-level ensembles for speech recognition

Bayesian multitask inverse reinforcement learning

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

Preference elicitation and inverse reinforcement learning

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

Bayesian variable order Markov models

Context model inference for large or partially observable MDPs

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.

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

Statistical decision making for authentication and intrusion detection

Intrusion detection using cost-sensitive classification

Tree exploration for bayesian RL exploration

Algorithms and Bounds for Sampling-based Approximate Policy Iteration

Exploration in POMDPs

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

Rollout sampling approximate policy iteration

Ensembles for sequence learning

Nearly Optimal Exploration-Exploitation Decision Thresholds

Online adaptive policies for ensemble classifiers

Gradient-based Estimates of Return Distributions

Boosting word error rates

Boosting HMMs with an application to speech recognition