Trevor Hastie and Robert Tibshirani. Varying-coefficient models. Journal of the Royal Statistical Society: Series B (Methodological), 55:757-796, 1993.
Trevor Hastie, Robert Tibshirani, and Martin Wainwright. Statistical Learning with Sparsity: The Lasso and Generalizations. CRC Press, 2015.
Trevor Hastie, Robert Tibshirani, and Ryan Tibshirani. Best subset, forward stepwise or lasso? Analysis and recommendations based on extensive comparisons. Statistical Science, 35:579-592, 2020.
Michael J Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi, Marco Concetto Rudilosso, Neethu Mariya Joy, Tejan Karmali, Avik Pal, and Viral B Shah. Fashionable modelling with Flux. In Workshop on Systems for ML and Open Source Software at NeurIPS 2018, 2018.
Diederik P Kingma and Jimmy Lei Ba. Adam: A method for stochastic optimization. In International Conference on Learning Representations, 2015.
Thibault Laugel, Marie-Jeanne Lesot, Christophe Marsala, Xavier Renard, and Marcin Detyniecki. The dangers of post-hoc interpretability: Unjustified counterfactual explanations. Proceedings of the 28th International Joint Conference on Artificial Intelligence, pages 2801-2807, 2019.
Ismael Lemhadri, Feng Ruan, Louis Abraham, and Robert Tibshirani. Lassonet: A neural network with feature sparsity. Journal of Machine Learning Research, 22:1-29, 2021a.
Ismael Lemhadri, Feng Ruan, and Robert Tibshirani. Lassonet: Neural networks with feature sparsity. In Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, volume 130, pages 10-18, 2021b.
Scott M Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems, volume 30, 2017.
Ricards Marcinkevics and Julia E Vogt. Interpretable and explainable machine learning: A method-centric overview with concrete examples. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 13, 2023.
Lukas Meier, Sara van de Geer, and Peter Buhlmann. The group lasso for logistic regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70:53-71, 2008.
Nicolai Meinshausen. Relaxed lasso. Computational Statistics and Data Analysis, 52:374-393, 2007.
Christoph Molnar, Giuseppe Casalicchio, and Bernd Bischl. Interpretable machine learning – A brief history, state-of-the-art and challenges. In ECML PKDD 2020 Workshops, volume 1323, pages 417–431, 2020.
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu. Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences of the United States of America, 116:22071–22080, 2019.
J A Nelder and R W M Wedderburn. Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135:370-384, 1972.
Byeong U Park, Enno Mammen, Young K Lee, and Eun Ryung Lee. Varying coefficient regression models: A review and new developments. International Statistical Review, 83:36-64, 2015.
Garvesh Raskutti, Martin J Wainwright, and Bin Yu. Minimax rates of estimation for high-dimensional linear regression over  $\ell_q$ -balls. IEEE Transactions on Information Theory, 57(10):6976-6994, 2011.
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, 2016.
Cynthia Rudin. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1:206-215, 2019.