References¶
The bibliography below is shared between the theory pages and any
docstring citations. The underlying file is docs/refs.bib, intended
to be reusable for a future paper.
Amir Beck and Marc Teboulle. A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM Journal on Imaging Sciences, 2(1):183–202, 2009.
Quentin Bertrand, Quentin Klopfenstein, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, and Joseph Salmon. Implicit differentiation of Lasso-type models for hyperparameter optimization. In Proceedings of the 37th International Conference on Machine Learning (ICML), volume 119 of Proceedings of Machine Learning Research, 810–821. 2020.
Quentin Bertrand, Quentin Klopfenstein, Mathurin Massias, Mathieu Blondel, Samuel Vaiter, Alexandre Gramfort, and Joseph Salmon. Implicit differentiation for fast hyperparameter selection in non-smooth convex learning. Journal of Machine Learning Research, 23(149):1–43, 2022.
Jérôme Bolte, Tam Le, Edouard Pauwels, and Antonio Silveti-Falls. Nonsmooth implicit differentiation for machine-learning and optimization. In Advances in Neural Information Processing Systems (NeurIPS), volume 34. 2021.
Charles-Alban Deledalle, Samuel Vaiter, Jalal Fadili, and Gabriel Peyré. Stein Unbiased GrAdient estimator of the Risk (SUGAR) for multiple parameter selection. SIAM Journal on Imaging Sciences, 7(4):2448–2487, 2014.
Jerome Friedman, Trevor Hastie, and Robert Tibshirani. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1):1–22, 2010.
Trevor Hastie, Robert Tibshirani, and Martin Wainwright. Statistical Learning with Sparsity: The Lasso and Generalizations. Chapman & Hall/CRC, 2015.
Steven G. Krantz and Harold R. Parks. The Implicit Function Theorem: History, Theory, and Applications. Birkhäuser, reprint of the 2003 edition edition, 2013.
Mathurin Massias, Alexandre Gramfort, and Joseph Salmon. Celer: a fast solver for the Lasso with dual extrapolation. In Proceedings of the 35th International Conference on Machine Learning (ICML), volume 80 of Proceedings of Machine Learning Research, 3315–3324. 2018.
Neal Parikh and Stephen Boyd. Proximal Algorithms. Volume 1. Foundations and Trends in Optimization, 2014.
Fabian Pedregosa. Hyperparameter optimization with approximate gradient. In Proceedings of the 33rd International Conference on Machine Learning (ICML), volume 48 of Proceedings of Machine Learning Research, 737–746. 2016.
Charles M. Stein. Estimation of the mean of a multivariate normal distribution. The Annals of Statistics, 9(6):1135–1151, 1981.
Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1):267–288, 1996.
Ming Yuan and Yi Lin. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society: Series B, 68(1):49–67, 2006.
Hui Zou. The adaptive lasso and its oracle properties. Journal of the American Statistical Association, 101(476):1418–1429, 2006.
Hui Zou and Trevor Hastie. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B, 67(2):301–320, 2005.
Hui Zou, Trevor Hastie, and Robert Tibshirani. On the “degrees of freedom” of the lasso. The Annals of Statistics, 35(5):2173–2192, 2007.