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...
| Autores principales: | , , |
|---|---|
| Formato: | Conference item |
| Lenguaje: | English |
| Publicado: |
International Joint Conferences on Artificial Intelligence
2022
|