CMS physicist here! As our ATLAS colleagues, we also use boosted decision trees extensively in our analysis, as well as the so-called Matrix Element Method -- a way to combine the knowledge of the differential theoretical cross-sections for the signal and background processes with our knowledge of the experimental resolution of our detectors. More details are available in [1].

However, in CMS we have recently started using Deep Neural Networks. These are used for the separation of signal and background events in the "single-lepton channel" of the search, i.e., the search where the Higgs boson decays to two bottom quarks and the top quarks decay in such a way that at least one electron or muon is present in the final state. Again, more details here: [2]

[1] http://arxiv.org/abs/1803.05485

[2] http://cms-results.web.cern.ch/cms-results/public-results/pr...

I was a bit involved in the ATLAS result. IMHO, it's not really that fancy. Basically, there's a boosted decision tree that is used to select signal-like events, and it was re-optimized some new features and a larger training set. Also, they dropped a statistical categorization that was of interest for a related process in favor maximizing sensitivity to this ttH production mechanism specifically.

I haven't looked too carefully at the CMS result, so no comment there.