up

Author: idc9

Readme.md

@@ -25,13 +25,13 @@ the following basic penalties
 and the following more sophisticated penalties
 
 - [Elastic net](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html) versions of the above
-- Adaptive Lasso versions of the above (including multi-task, group and nuclear norm)
+- [Adaptive Lasso](http://users.stat.umn.edu/~zouxx019/Papers/adalasso.pdf) versions of the above (including multi-task, group and nuclear norm)
 - Folded concave penalties (FCP) such as [SCAD](https://fan.princeton.edu/papers/01/penlike.pdf) fit by applying the *local linear approximation* (LLA) algorithm to a "good enough" initializer such as the Lasso fit ([Zou and Li, 2008](http://www.personal.psu.edu/ril4/research/AOS0316.pdf); [Fan et al, 2014](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295817/)). We also provide concave versions of the group Lasso, multi-task Lasso and nuclear norm that are not discussed in the original paper.
 
 
 The built in cross-validation functionality supports
 
-- faster path algorithms for convex loss functions (as in sklearn.linear_model.LassoCV)
+- faster path algorithms for convex penalties and adaptive lasso (e.g. as in [sklearn.linear_model.LassoCV](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LassoCV.html))
 - automatically generated tuning parameter path for any loss + penalty combination
 - custom evaluation metrics
 - custom selection rules such as the '1se' rule from the glmnet package
@@ -148,9 +148,12 @@ bug fixes, spelling errors, new features, etc.
 
 # References
 
-Beck, A. and Teboulle, M., 2009. [A fast iterative shrinkage-thresholding algorithm for linear inverse problems](https://epubs.siam.org/doi/pdf/10.1137/080716542?casa_token=cjyK5OxcbSoAAAAA:lQOp0YAVKIOv2-vgGUd_YrnZC9VhbgWvZgj4UPbgfw8I7NV44K82vbIu0oz2-xAACBz9k0Lclw). SIAM journal on imaging sciences, 2(1), pp.183-202.
+
+
+Zou, H., 2006. [The adaptive lasso and its oracle properties](http://users.stat.umn.edu/~zouxx019/Papers/adalasso.pdf). Journal of the American statistical association, 101(476), pp.1418-1429.
 
 Zou, H. and Li, R., 2008. [One-step sparse estimates in nonconcave penalized likelihood models](http://www.personal.psu.edu/ril4/research/AOS0316.pdf). Annals of statistics, 36(4), p.1509.
 
+Beck, A. and Teboulle, M., 2009. [A fast iterative shrinkage-thresholding algorithm for linear inverse problems](https://epubs.siam.org/doi/pdf/10.1137/080716542?casa_token=cjyK5OxcbSoAAAAA:lQOp0YAVKIOv2-vgGUd_YrnZC9VhbgWvZgj4UPbgfw8I7NV44K82vbIu0oz2-xAACBz9k0Lclw). SIAM journal on imaging sciences, 2(1), pp.183-202.
 
 Fan, J., Xue, L. and Zou, H., 2014. [Strong oracle optimality of folded concave penalized estimation](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295817/). Annals of statistics, 42(3), p.819.