I would agree that a precise definition is hard to come by. My understanding (which others, e.g., seminar participants also seem to be agreed with) is the following: "principled" refers to the fact that you let yourself be disciplined by an algorithm/a procedure (to for example choose tuning parameters) to select your predictors rather than handpick them so as to, for example, produce an impressively low error on your training data set. Similarly, if you were (which is not often the case in ML) interested to confirm some theory, an unprincipled way of choosing predictors would be to try many models until one comes out where your coefficients of interested have the desires sign and statistical significance.

I would agree that a precise definition is hard to come by. My understanding (which others, e.g., seminar participants also seem to agree with) is the following: "principled" refers to the fact that you let yourself be disciplined by an algorithm/a procedure (to for example choose tuning parameters) to select your predictors rather than handpick them so as to, for example, produce an impressively low error on your training data set. 

Similarly, if you were (which is not so often the case in ML) interested in confirming some theory, an unprincipled way of choosing predictors would be to try many models until one comes out where your coefficients of interest have the desired sign and statistical significance.