[4] K. Ahuja, K. Shanmugam, K. Varshney, and A. Dhurandhar. Invariant risk minimization game. In International Conference on Machine Learning, 2020.
[5] E. Amparore, A. Perotti, and P. Bajardi. To trust or not to trust an explanation: using leaf to evaluate local linear xai methods. PeerJ Computer Science, 7:e479, 2021.
[6] C. J. Anders, P. Pasliev, A.-K. Dombrowski, K.-R. Muller, and P. Kessel. Fairwashing explanations with off-manifold detergent. In Intl. Conference on Machine Learning (ICML), 2020.
[7] M. Arjovsky, L. Bottou, I. Gulrajani, and D. Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019.
[8] T. Botari, F. Hvilshøj, R. Izbicki, and A. C. P. L. F. de Carvalho. Melime: Meaningful local explanation for machine learning models, 2020.
[9] J. Chen, X. Wu, V. Rastogi, Y. Liang, and S. Jha. Robust attribution regularization. NeurIPS, 2019.
[10] E. Creager, J.-H. Jacobsen, and R. Zemel. Environment inference for invariant learning. In e ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning, 2020.
[11] G. Debreu. A social equilibrium existence theorem. Proceedings of the National Academy of Sciences, 38(10):886-893, 1952.
[12] D. Dheeru and E. Karra Taniskidou. UCI machine learning repository, 2017.
[13] A. Dhurandhar, P.-Y. Chen, R. Luss, C.-C. Tu, P. Ting, K. Shanmugam, and P. Das. Explanations based on the missing: Towards contrastive explanations with pertinent negatives. In Advances in Neural Information Processing Systems, pages 592-603, 2018.
[14] A. Dhurandhar, T. Pedapati, A. Balakrishnan, K. A. Pin-Yu Chen, Karthikeyan Shanmugam, and R. Puri. Model agnostic contrastive explanations for structured data. https://arxiv.org/abs/1906.00117, 2019.
[15] A. Dhurandhar, K. N. Ramamurthy, and K. Shanmugam. Is this the right neighborhood? accurate and query efficient model agnostic explanations. In Advances in Neural Information Processing Systems, 2022.
[16] A. Dhurandhar, K. Shanmugam, and R. Luss. Enhancing simple models by exploiting what they already know. In Intl. Conference on Machine Learning, 2020.
[17] A. Dhurandhar, K. Shanmugam, R. Luss, and P. Olsen. Improving simple models with confidence profiles. In Advances in Neural Information Processing Systems, pages 10296-10306, 2018.
[18] A.-K. Dombrowski, M. Alber, C. J. Anders, M. Ackermann, K.-R. Müller, and P. Kessel. Explanations can be manipulated and geometry is to blame. In Advances in Neural Information Processing Systems, 2019.
[19] A.-K. Dombrowski, C. J. Anders, K.-R. Müller, and P. Kessel. Towards robust explanations for deep neural networks. 121(C), 2022.
[20] P. K. Dutta. Strategies and games: theory and practice. MIT Press, 1999.
[21] S. Feng, E. Wallace, A. Grissom II, M. Iyyer, P. Rodriguez, and J. Boyd-Graber. Pathologies of neural models make interpretations difficult. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3719–3728, Brussels, Belgium, Oct.-Nov. 2018. Association for Computational Linguistics.
[22] C. Frye, C. Rowat, and I. Feige. Asymmetric shapley values: incorporating causal knowledge into model-agnostic explainability. NeurIPS, 2020.
[23] A. Ghorbani, A. Abid, and J. Zou. Interpretation of neural networks is fragile, 2019.