Add a Differentially-Private AdaBoost model

[UPstartDeveloper]

Is your feature request related to a problem? Please describe.
I've been trying to understand the paper, "Efficient, Noise-Tolerant, and Private Learning via Boosting" and it is difficult as the work is purely theoretical.

Describe the solution you'd like
I want to have an actual implementation of the boosting framework presented in this ^paper, for the purpose of furthering my own understanding.

Describe alternatives you've considered
So far it looks like TensorFlow Decision Forests has a module similar to what I'm looking for - but it looks like overkill to use it, subclass it, all to use a new kind of optimization algorithm (based on the paper I'm reading).

Additional context

• I'm interested to understand this library better, so I've actually started trying to implement this on my fork. So far it includes (what I think) is the code for the model) and a demo notebook.
• The main blockers I think I have right now are the following:

1. reproducibility - when trying to run my current implementation (i.e., in adaboost.py) in the notebook (see cell 12 of plot_adaboost_twoclass.ipynb), it always seems to "bounce around" in training accuracy. E.g. 43% for the first run, then 47%, and eventually 0.00% within ~5 runs. I'm trying to understand if this is an expected result, and if not what can be done to fix it?
2. testing - how should I go about writing tests for the adaboost.py?

Any other feedback/questions are appreciated!