Pavan Ramkumar

according to the paper, the logic of warm restarts doesn't work if we go from low to high lambda. they recommend fitting the largest lambda first and initializing the next...

@hugoguh agreed that we should make the outputs conform to sklearn and R glmnet package for whichever use cases exist in those tools. I appreciate all the testing!

Let's aim to get to the bottom of the bug. In your bug report above, I wouldn't call it a convergence issue, so much as a poor choice of reg....

As for the linear case with Roozbeh's data, we figured out that the fit improves a lot with a better choice of learning rate. We can only get to the...

From here: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html alpha : float Constant that multiplies the penalty terms. Defaults to 1.0 See the notes for the exact mathematical meaning of this parameter. alpha = 0 is...

Turns out this is mainly an issue of regularization path. See conversation in #76 Should I create new issue for "solution dependence on regularization path"?

hmm, looks like it didn't budge. merge or close?

@jasmainak it's really strange why the dataset fetcher doesn't work with travis. when i run it on my local py35 evironment, it works fine. perhaps a miniconda dependency issue? ```...

> it should go down monotonically > > this could explain why we don't see comparable results i saw the same issue in convergence plots when i tried to reproduce...

showing poisson vs neg bin fits in our example could be useful. i also like this example: https://data.library.virginia.edu/getting-started-with-negative-binomial-regression-modeling/ it talks about where poisson assumptions are lacking and how neg bin...