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Safety in learned controllers
Safety constraints for learned controllers with known dynamics.
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BarrierNet: A Safety-Guaranteed Layer for Neural Networks
A strategy for encoding constraints on the state space of a control problem as a differentiable layer for neural network based controllers …
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Safety and reliability in ML systems
Anomaly detection
Many industrial applications of automated decision-making involve the detection of anomalous behaviour. Precisely defining what this means is a complex problem hindered by strong class imbalances, lack of useful models or changing …
6 items
Formal verification under uncertainty
Being able to ensure that AI systems behave as intended is essential for their adoption in many areas. This series covers formal methods for the verification of safety, fairness, and other relevant properties of (probabilistic) ML systems. …
2 items
Robustness in ML
The robustness of a model is a measure of its stability with respect to perturbations of the input. We investigate and review recent developments in the field affecting the training, evaluation and deployment of ML systems.
6 items
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