End-to-End Learning for Retrospective Change-Point Estimation

We propose an approach to retrospective change-point estimation that includes learning feature representations from data. The feature representations are specified within a differentiable programming framework, that is, as parameterized mappings amenable to automatic differentiation. The proposed method uses these feature representations in a penalized least-squares objective into which known change-point labels can be incorporated. We propose to minimize the objective using an alternating optimization procedure. We present numerical illustrations on synthetic and real data showing that learning feature representations can result in more accurate estimation of change-point locations.