#machine-learning-safety News & Analysis

2 articles tagged with #machine-learning-safety. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

2 articles

AIBullisharXiv – CS AI · Apr 137/10

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SafeAdapt: Provably Safe Policy Updates in Deep Reinforcement Learning

Researchers introduce SafeAdapt, a novel framework for updating reinforcement learning policies while maintaining provable safety guarantees across changing environments. The approach uses a 'Rashomon set' to identify safe parameter regions and projects policy updates onto this certified space, addressing the critical challenge of deploying RL agents in safety-critical applications where dynamics and objectives evolve over time.

AIBullisharXiv – CS AI · Mar 35/106

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Learning to Explore: Policy-Guided Outlier Synthesis for Graph Out-of-Distribution Detection

Researchers propose PGOS (Policy-Guided Outlier Synthesis), a new framework that uses reinforcement learning to improve Graph Neural Network safety by better detecting out-of-distribution graphs. The system replaces static sampling methods with a learned exploration strategy that navigates low-density regions to generate pseudo-OOD graphs for enhanced detector training.