Post-hoc Rule Based Explanations for Black Box Bayesian Optimization

Author:

Chakraborty TanmayORCID,Wirth Christian,Seifert ChristinORCID

Publisher

Springer Nature Switzerland

Reference46 articles.

1. Amoukou, S.I., Brunel, N.J.: Consistent sufficient explanations and minimal local rules for explaining the decision of any classifier or regressor. In: Advances in Neural Information Processing Systems, NeurIPS. Curran Associates, Inc. (2022)

2. Bénard, C., Biau, G., Veiga, S.D., Scornet, E.: Interpretable random forests via rule extraction. In: Proceedings of International Conference on Artificial Intelligence and Statistics, AISTATS, pp. 937–945. PMLR (2021)

3. Brochu, E., Cora, V.M., de Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. CoRR abs/1012.2599 (2010)

4. Chakraborty, T., Trehan, U., Mallat, K., Dugelay, J.: Generalizing adversarial explanations with Grad-CAM. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops, pp. 186–192. Computer Vision Foundation/IEEE (2022)

5. Coppens, Y., et al.: Distilling deep reinforcement learning policies in soft decision trees. In: Proceedings of the Workshop on Explainable Artificial Intelligence, IJCAI Workshop, pp. 1–6. IJCAI (2019)

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