We present a fully automatic system for ranking domain-specific highlights in unconstrained personal videos by analyzing online edited videos. A novel latent linear ranking model is proposed to handle noisy training data harvested online. Specifically, given a search query (domain) such as “surfing”, our system mines the Youtube database to find pairs of raw and corresponding edited videos. Leveraging the assumption that edited video is more likely to contain highlights than the trimmed parts of the raw video, we obtain pair-wise ranking constraints to train our model. The learning task is challenging due to the amount of noise and variation in the mined data. Hence, a latent loss function is incorporated to robustly deal with the noise. We efficiently learn the latent model on a large number of videos (about 700 minutes in all) using a novel EM-like self-paced model selection procedure. Our latent ranking model outperforms its classification counterpart, a motion analysis baseline [15], and a fully-supervised ranking system that requires labels from Amazon Mechanical Turk. Finally, we show that impressive highlights can be retrieved without additional human supervision for domains like skating, surfing, skiing, gymnastics, parkour, and dog activity in unconstrained personal videos.