[41] Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. "why should i trust you?": Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, page 1135-1144, San Francisco California USA, Aug 2016. ACM.
[42] Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. From local explanations to global understanding with explainable ai for trees. Nature machine intelligence, 2(1):56-67, 2020.
[43] Haoran Zhang, Quaid Morris, Berk Ustun, and Marzyeh Ghassemi. Learning optimal predictive checklists. Advances in Neural Information Processing Systems, 34:1215-1229, 2021.
[44] Indro Spinelli, Simone Scardapane, and Aurelio Uncini. A meta-learning approach for training explainable graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, page 1-9, 2022.
[45] Theodoros Tsiligkaridis and Jay Roberts. Understanding and increasing efficiency of frankwolfe adversarial training. In CVPR, 2022.
[46] Jay Roberts and Theodoros Tsiligkaridis. Controllably Sparse Perturbations of Robust Classifiers for Explaining Predictions and Probing Learned Concepts. In Daniel Archambault, Ian Nabney, and Jaakko Peltonen, editors, Machine Learning Methods in Visualisation for Big Data. The Eurographics Association, 2021.
[47] Jay Roberts and Theodoros Tsiligkaridis. Ultrasound diagnosis of Covid-19: Robustness and explainability. In Medical Imaging meets NeurIPS Workshop, 2020.
[48] Ian E. Nielsen, Dimah Dera, Ravi P. Ramachandran Ghulam Rasool, and Nidhal Carla Bouay. Robust explainability: A tutorial on gradient-based attribution methods for deep neural networks. IEEE Signal Processing Magazine, 39(4), July 2022.
[49] Jasmijn Bastings, Wilker Aziz, and Ivan Titov. Interpretable neural predictions with differentiable binary variables. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2963-2977, Florence, Italy, July 2019. Association for Computational Linguistics.
[50] Siqi Miao, Mia Liu, and Pan Li. Interpretable and generalizable graph learning via stochastic attention mechanism. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato, editors, Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pages 15524-15543. PMLR, 17-23 Jul 2022.
[51] Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K Su. This looks like that: deep learning for interpretable image recognition. Advances in neural information processing systems, 32, 2019.
[52] Srishti Gautam, Ahcene Boubekki, Stine Hansen, Suaiba Salahuddin, Robert Jenssen, Marina Höhne, and Michael Kampffmeyer. Protovae: A trustworthy self-explainable prototypical variational model. Advances in Neural Information Processing Systems, 35:17940-17952, 2022.
[53] Irina Higgins, Nicolas Sonnerat, Loic Matthew, Arka Pal, Christopher P Burgess, Matko Bosnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, and Alexander Lerchner. Scan: Learning hierarchical compositional visual concepts. arXiv preprint arXiv:1707.03389, 2017.
[54] Tailin Wu, Megan Tjandrasuwita, Zhengxuan Wu, Xuelin Yang, Kevin Liu, Rok Sosic, and Jure Leskovec. Zeroc: A neuro-symbolic model for zero-shot concept recognition and acquisition at inference time. In Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho, editors, Advances in Neural Information Processing Systems, 2022.
[55] Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B Tenenbaum, and Jiajun Wu. The neurosymbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. International Conference on Learning Representations, 2019.
[56] Prathyush S Parvatharaju, Ramesh Doddiah, Thomas Hartvigsen, and Elke A Rundensteiner. Learning saliency maps to explain deep time series classifiers. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pages 1406-1415, 2021.