Search Results for author:
Found 5 papers, 0 papers with code
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system development and have played a key role in establishing the correctness of safety-critical systems.
Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning
Accordingly, we propose Privacy-Engineered Value Decomposition Networks (PE-VDN), a Co-MARL algorithm that models multi-agent coordination while provably safeguarding the confidentiality of the agents' environment interaction data.
Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning
We study the privacy risks that are associated with training a neural network's weights with self-supervised learning algorithms.
On the Privacy Risks of Deploying Recurrent Neural Networks in Machine Learning Models
Additionally, we study the effectiveness of two prominent mitigation methods for preempting MIAs, namely weight regularization and differential privacy.
Privacy-Preserving Kickstarting Deep Reinforcement Learning with Privacy-Aware Learners
We then develop a kickstarted deep reinforcement learning algorithm for the student that is privacy-aware because we calibrate its objective with the parameters of the teacher's privacy mechanism.
