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I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JASA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, and neural networks, although this area is less active now. Statisticians have appropriated the work of Valiant and Vapnik, but on the other side, computer scientists have absorbed the work of Donoho and Talagrand. I don't think there is much difference in scope and methods any more. I have never bought Breiman's argument that CS people were only interested in minimizing loss using whatever works. That view was heavily influenced by his participation in Neural Networks conferences and his consulting work; but PAC, SVMs, Boosting have all solid foundations. And today, unlike in 2001, Statistics is more concerned with finite-sample properties, algorithms and massive datasets.

But I think that there are still three important differences that are not going away soon.

1. Methodological Statistics papers are still overwhelmingly formal and deductive, whereas Machine Learning researchers are more tolerant of new approaches even if they don't come with a proof attached;
2. The ML community primarily shares new results and publications in conferences and related proceedings, whereas statisticians use journal papers. This slows down progress in Statistics and identification of star researchers. John Langford a nice post on the subject from a while back;
3. Statistics still covers areas that are (for now) of little concern to ML, such as survey design, sampling, industrial Statistics etc.

I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JASA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, and neural networks, although this area is less active now. Statisticians have appropriated the work of Valiant and Vapnik, but on the other side, computer scientists have absorbed the work of Donoho and Talagrand. I don't think there is much difference in scope and methods any more. I have never bought Breiman's argument that CS people were only interested in minimizing loss using whatever works. That view was heavily influenced by his participation in Neural Networks conferences and his consulting work; but PAC, SVMs, Boosting have all solid foundations. And today, unlike 2001, Statistics is more concerned with finite-sample properties, algorithms and massive datasets.

But I think that there are still three important differences that are not going away soon.

1. Methodological Statistics papers are still overwhelmingly formal and deductive, whereas Machine Learning researchers are more tolerant of new approaches even if they don't come with a proof attached;
2. The ML community primarily shares new results and publications in conferences and related proceedings, whereas statisticians use journal papers. This slows down progress in Statistics and identification of star researchers. John Langford has a nice post on the subject from a while back;
3. Statistics still covers areas that are (for now) of little concern to ML, such as survey design, sampling, industrial Statistics etc.

I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JAMA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, and neural networks, although this area is less active now. Statisticians have appropriated the work of Valiant and Vapnick, but on the other side, computer scientists have absorbed the work of Donoho and Talagrand. I don't think there is much difference in scope and methods anymore. I have never bought Breiman's argument that CS people were only interested in minimizing loss using whatever works. That view was heavily influenced by his participation in Neural Networks conferences and his consulting work; but PAC, SVMs, Boosting have all solid foundations. And today, unlike in 2001, Statistics is more concerned with finite-sample properties, algorithms and massive datasets.

But I think that there are still three important differences that are not going away soon.

1. Methodological Statistics papers are still overwhelmingly formal and deductive, whereas Machine Learning researchers are more tolerant of new approaches even if they don't come with a proof attached;
2. The ML community primarily shares new results and publications in conferences and related proceedings, whereas statisticians use journal papers. This slows down progress in Statistics and identification of star researchers. John Langford a nice post on the subject from a while back;
3. Statistics still covers areas that are (for now) of little concern to ML, such as survey design, sampling, industrial Statistics etc.

I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JASA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, and neural networks, although this area is less active now. Statisticians have appropriated the work of Valiant and Vapnik, but on the other side, computer scientists have absorbed the work of Donoho and Talagrand. I don't think there is much difference in scope and methods any more. I have never bought Breiman's argument that CS people were only interested in minimizing loss using whatever works. That view was heavily influenced by his participation in Neural Networks conferences and his consulting work; but PAC, SVMs, Boosting have all solid foundations. And today, unlike in 2001, Statistics is more concerned with finite-sample properties, algorithms and massive datasets.

But I think that there are still three important differences that are not going away soon.

1. Methodological Statistics papers are still overwhelmingly formal and deductive, whereas Machine Learning researchers are more tolerant of new approaches even if they don't come with a proof attached;
2. The ML community primarily shares new results and publications in conferences and related proceedings, whereas statisticians use journal papers. This slows down progress in Statistics and identification of star researchers. John Langford a nice post on the subject from a while back;
3. Statistics still covers areas that are (for now) of little concern to ML, such as survey design, sampling, industrial Statistics etc.

I think the answer to your first question is simply in "yes". Take any issue of Statistical Science, JAMA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, and neural networks, although this area is less active now. Statisticians have appropriated the work of Valiant and Vapnick, but on the other side, computer scientists have absorbed the work of Donoho and Talagrand. I don't think there is much difference in scope and methods anymore. I have never bought Breiman's argument that CS people were only interested in minimizing loss using whatever works. That view was heavily influenced by his participation in Neural Networks conferences and his consulting work; but PAC, SVMs, Boosting have all solid foundations. And today, unlike in 2001, Statistics is more concerned with finite-sample properties, algorithms and massive datasets.

But I think that there are still three important differences that are not going away soon.

1. Methodological Statistics papers are still overwhelmingly formal and deductive, whereas Machine Learning researchers are more tolerant of new approaches even if they don't come with a proof attached;
2. The ML community primarily shares new results and publications in conferences and related proceedings, whereas statisticians use journal papers. This slows down progress in Statistics and identification of star researchers. John Langford a nice post on the subject from a while back;
3. Statistics still covers areas that are (for now) of little concern to ML, such as survey design, sampling, industrial Statistics etc.

I think the answer to your first question is simply in the affirmative. Take any issue of Statistical Science, JAMA, Annals of Statistics of the past 10 years and you'll find papers on boosting, SVM, and neural networks, although this area is less active now. Statisticians have appropriated the work of Valiant and Vapnick, but on the other side, computer scientists have absorbed the work of Donoho and Talagrand. I don't think there is much difference in scope and methods anymore. I have never bought Breiman's argument that CS people were only interested in minimizing loss using whatever works. That view was heavily influenced by his participation in Neural Networks conferences and his consulting work; but PAC, SVMs, Boosting have all solid foundations. And today, unlike in 2001, Statistics is more concerned with finite-sample properties, algorithms and massive datasets.

But I think that there are still three important differences that are not going away soon.

1. Methodological Statistics papers are still overwhelmingly formal and deductive, whereas Machine Learning researchers are more tolerant of new approaches even if they don't come with a proof attached;
2. The ML community primarily shares new results and publications in conferences and related proceedings, whereas statisticians use journal papers. This slows down progress in Statistics and identification of star researchers. John Langford a nice post on the subject from a while back;
3. Statistics still covers areas that are (for now) of little concern to ML, such as survey design, sampling, industrial Statistics etc.