<p><strong>Yes.</strong></p>
<p>Our paper summarizing this discussion: <a href="https://stats.stackexchange.com/questions/328630">Is ridge regression useless in high dimensions ($n \ll p$)? How can OLS fail to overfit?</a> has just been published by <em>The Journal of Machine Learning Research</em>:</p>
<blockquote>
<p>The Optimal Ridge Penalty for Real-world High-dimensional Data Can Be Zero or Negative due to the Implicit Ridge Regularization<br />
<em>Dmitry Kobak, Jonathan Lomond, Benoit Sanchez; 21(169):1−16, 2020.</em><br />
<a href="https://jmlr.org/papers/v21/19-844.html" rel="nofollow noreferrer">https://jmlr.org/papers/v21/19-844.html</a></p>
</blockquote>
<p>It was a preprint since mid-2018: <a href="https://arxiv.org/abs/1805.10939" rel="nofollow noreferrer">https://arxiv.org/abs/1805.10939</a> but took some time to publish formally (and got substantially extended over the course of revisions). JMLR is a very respectable journal, so I am happy :-)</p>
<p>We of course acknowledge CrossValidated:</p>
<p><a href="https://i.stack.imgur.com/4FKAk.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/4FKAk.png" alt="enter image description here" /></a></p>
<p>@amoeba, @JonnyLomond, @BenoitSanchez</p>
