You should keep in mind that probably none of those machine-learning researchers has studied only math specific to that domain, so their papers are likely to include whatever math they have a background in, plus any new techniques they had to learn to get their results.
That said, everything I saw in the papers you linked was linear algebra, calculus or probability theory plus the usual smattering of background notation and set theory.
Once you have a solid background in those areas, it is likely more productive to look up the specific concepts mentioned in a paper (such as the Kullback-Leibler divergence or the Bellman equation), because by then you are probably too deep in the woods to find one resource that adequately covers all those different directions.
That said, everything I saw in the papers you linked was linear algebra, calculus or probability theory plus the usual smattering of background notation and set theory.
Once you have a solid background in those areas, it is likely more productive to look up the specific concepts mentioned in a paper (such as the Kullback-Leibler divergence or the Bellman equation), because by then you are probably too deep in the woods to find one resource that adequately covers all those different directions.