Robustness guarantees for credal Bayesian networks via constraint relaxation over probabilistic circuits
In many domains, worst-case guarantees on the performance (e.g. prediction accuracy) of a decision function subject to distributional shifts and uncertainty about the environment are crucial. In this work we develop a method to quantify the robustness of decision functions with respect to credal Bay...
| Main Authors: | , , |
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| Format: | Conference item |
| Language: | English |
| Published: |
International Joint Conferences on Artificial Intelligence
2022
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