1. Agarwal, R., Melnick, L., Frosst, N., Zhang, X., Lengerich, B., Caruana, R., & Hinton, G. (2021a). Neural additive models: Interpretable machine learning with neural nets. In Advances in Neural Information Processing Systems. Virtual. Available from: https://openreview.net/forum?id=wHkKTW2wrmm
2. Agrawal, A., Batra, D., Parikh, D., & Kembhavi, A. (2018). Don’t just assume; look and answer: Overcoming priors for visual question answering. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Available from: https://openaccess.thecvf.com/content_cvpr_2018/papers/Agrawal_Dont_Just_Assume_CVPR_2018_paper.pdf
3. Agresti, A. (2002). Categorical data analysis. Wiley. Available from: https://doi.org/10.1002/0471249688
4. Alvarez-Melis, D., & Jaakkola, T. S. (2018). Towards robust interpretability with self-explaining neural networks. In Proceedings of the 32nd Conference on Neural Information Processing Systems (NeurIPS). Available from: https://proceedings.neurips.cc/paper_files/paper/2018/file/3e9f0fc9b2f89e043bc6233994dfcf76-Paper.pdf
5. Amodio, S., Aria, M., & D’Ambrosio, A. (2014). On concurvity in nonlinear and nonparameric regression models. Statistica, 1, 85–98. Available from: http://dx.doi.org/10.6092/issn.1973-2201/4599