Christos Dimitrakakis
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Visar 81 publikationer
Reinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
Minimax-Bayes Reinforcement Learning
Approximate Inference for the Bayesian Fairness Framework
Minimax-Bayes Reinforcement Learning
Risk-Sensitive Bayesian Games for Multi-Agent Reinforcement Learning under Policy Uncertainty
Interactive Inverse Reinforcement Learning for Cooperative Games
SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learning
Inferential Induction: A Novel Framework for Bayesian Reinforcement Learning
Bayesian Reinforcement Learning via Deep, Sparse Sampling
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
Multi-view decision processes: The helper-AI problem
Differential privacy for Bayesian inference through posterior sampling
A Differentially Private Encryption Scheme
Thompson Sampling for Stochastic Bandits with Graph Feedback
Adaptive Sampling-based View Planning under Time Constraints
Achieving Privacy in the Adversarial Multi-Armed Bandit
Bayesian Inference for Least Squares Temporal Difference Regularization
Algorithms for Differentially Private Multi-Armed Bandits
On the differential privacy of Bayesian inference
Optimal advertisement strategies for small and big companies
Synergistic user ↔ context analytics
Distance-Bounding Protocols: Are You Close Enough?
Expected loss analysis for authentication in constrained channels
Foreword - Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security
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
Foreword - Proceedings of the ACM Conference on Computer and Communications Security
The reinforcement learning competition
Workshop Summary of AiSec'14-2014 Workshop on Artificial Intelligent and Security
Differential Privacy and Private Bayesian Inference.
Usable ABC reinforcement learning
Cover Tree Bayesian Reinforcement Learning
Personalized news recommendation with context trees
Probabilistic inverse reinforcement learning in unknown environments
Summary/overview for artificial intelligence and security (AISec'13)
Linear Bayesian reinforcement learning
Monte-Carlo utility estimates for Bayesian reinforcement learning
Proceedings of the ACM Conference on Computer and Communications Security: Foreword
On selecting the nonce length in distance bounding protocols
Generalization and Interference in Human Motor Control
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
Phoneme and sentence-level ensembles for speech recognition
Robust Bayesian reinforcement learning through tight lower bounds
Bayesian multitask inverse reinforcement learning
Preference elicitation and inverse reinforcement learning
Network Self-organization Explains the Distribution of Synaptic Efficacies in Neocortex
Reid et al.'s distance bounding protocol and mafia fraud attacks over noisy channels
Efficient methods for near-optimal sequential decision making under uncertainty
Context model inference for large or partially observable MDPs
Statistical decision making for authentication and intrusion detection
Intrusion detection using cost-sensitive classification
Algorithms and Bounds for Sampling-based Approximate Policy Iteration
Tree exploration for bayesian RL exploration
Cost-minimising strategies for data labelling: Optimal stopping and active learning
Rollout sampling approximate policy iteration
Nearly Optimal Exploration-Exploitation Decision Thresholds
Online adaptive policies for ensemble classifiers
Gradient-based Estimates of Return Distributions
Boosting HMMs with an application to speech recognition
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Visar 4 forskningsprojekt
Mekanismer för säkert och privat maskininlärning
Learning, privacy and the limits of computation
ACE: Approximativa algoritmer och datorsystem