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. We document the progress in research domains such as neuro-symbolic verification, probabilistic model-checking, and weighted model integration.

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Neuro Symbolic Verification of Deep Neural Networks

The neuro-symbolic approach to verification of neural networks uses reference networks to represent high level concepts. This allows the …

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Formal Verification Under Uncertainty
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Probabilistic Inference With Algebra and Logic Constraints

Hybrid probabilistic inference allows performing probabilistic inference with algebra and logic constraints. Recent advances with emphasis …

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Formal Verification Under Uncertainty
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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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Formal Verification Under Uncertainty
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Probabilistic Model Checking with Storm

A 1-day workshop introducing the concept of probabilistic model checking and its applications with the Storm probabilistic model checker.

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