weak supervision

Labels are the expensive bottleneck in ML, and these two techniques attack it from different angles. The signal is knowing active learning chooses what to label while semi-supervised uses unlabeled data directly. Here is the answer.

Hand-labeling at scale is the bottleneck. Weak supervision generates labels programmatically instead, and the signal is whether you can explain how a label model denoises conflicting sources into probabilistic labels. Here is the answer.

Many real problems give you confirmed positives but never confirmed negatives, only unlabeled data. The shortcut everyone reaches for quietly biases the model. The signal is naming the regime and its fix. Here is the answer.

Small labeled sets are the normal starting condition, not an excuse. The strong answer is a ladder of techniques that squeeze signal from unlabeled data, pretrained models, and the labeling budget. Here it is.