I highly doubt that a person with a PhD in statistics doesn't identify as a statistician.

Look at Vapnik’s publications and affiliations around this time (1990 to 1997). He was working at a research group at ATT Labs with people like Corinna Cortes, León Bottou, and Yann LeCun. He had just come over from Russia, as did many technically proficient Russian Jews in these years.

None of the people in this ATT Labs milieu were associated with the Statistics community. They didn’t go to Stats conferences, and they didn’t publish in Stats journals. I don’t believe that any of them were formally trained in what you might call conventional statistics approaches of the time, i.e., no Stanford PhD, no JASA or Ann. Stat. publications.

They knew about that stuff! But they were approaching problems, like digit recognition (which are not amenable to model based statistics) from a more applied math/physics point of view. Vapnik’s work that appeared in English at this time introduces learning as a form of Tikhonov regularization applied to a loss function. Not as a type of maximum likelihood, not as a riff on Bayesian inference. And his SVM work introduces the kernel trick as an interpretation of Mercer’s theorem — a very applied math motivation.

I knew Vladimir around this time, but I can’t really say if he would have described himself as a “statistician” — that would be hard for anyone to know. But I can say that he and his closest colleagues were not part of the Stats community of the time.

On the other side, very few Stats authorities were deeply interested in this stuff. Leo Brieman, of course, Andrew Barron, Art Owen, Trevor Hastie, Rob Tibshirani. It’s the Stats community’s loss that so few recognized the value of these approaches.

Beautifully summarized !

The only way to dispute that would be appeal to authority so I will avoid. Perhaps there was a time when he did identify as a mathematical/theoretical statistician, but his contributions were quite a dramatic break from what was the norm in statistical practice at the time.

I would argue that his contributions were central to the birth of rigorous machine learning (the non deep learning kind) as a field of its own with a focus that's different from that of statistics.

One quantitative test I can suggest -- one can count the number of occurrences of the word 'statistics' in the journals and conferences he has published and compare that with the number of publications he has authored. My sense is that it will be close to 0 and getting closer (if not already there yet).

> One quantitative test I can suggest -- one can count the number of occurrences of the word 'statistics' in the journals and conferences he has published and compare that with the number of publications he has authored. My sense is that it will be close to 0 and getting closer (if not already there yet).

I'm not sure this "quantitative test" is the best approach... after all, I'm pretty sure "Biometrica" and "Econometrica" don't have "statistics" in their names.

Fair point.

Some may identify as mathematicians.