Not all people share your point of view. :( From time to time, I review scientific papers for Machine Learning journals. In the modern machine learning theore

Not all people share your point of view. :( From time to time, I review scientific papers for Machine Learning journals. In the modern machine learning theoretical papers of the type: We looked in problem A, here is a pattern B and it is the case because <math.> Nearly all papers that I get for a review are applied papers: We took dataset A, model B, tweaked knob C and our result is better than paper D, but we do not know why. I am fine with this approach. Many good ML ideas were introduced in this way. But I give reject if authors did not provide the dataset and code that reproduces results. It is not an issue for other reviewers and my reject is often overridden by editors and papers get accepted, even if there is not way that anyone ever will be able to reproduce it.