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Found 3 papers, 1 papers with code
Preserving Privacy in Personalized Models for Distributed Mobile Services
We present Pelican, a privacy-preserving personalization system for context-aware mobile services that leverages both device and cloud resources to personalize ML models while minimizing the risk of privacy leakage for users.
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
Saliency maps are frequently used to support explanations of the behavior of deep reinforcement learning (RL) agents.
Measuring and Characterizing Generalization in Deep Reinforcement Learning
We re-examine what is meant by generalization in RL, and propose several definitions based on an agent's performance in on-policy, off-policy, and unreachable states.
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