Active Learning

Ordering training samples from easier to harder to improve convergence or generalization.

Expanding training data via transformations (flips, noise, paraphrases) to improve robustness.

Artificially created data used to train/test models; helpful for privacy and coverage, risky if unrealistic.

Training across many devices/silos without centralizing raw data; aggregates updates, not data.

A hidden variable influences both cause and effect, biasing naive estimates of causal impact.

Controlled experiment comparing variants by random assignment to estimate causal effects of changes.

Human or automated process of assigning targets; quality, consistency, and guidelines matter heavily.

Measure of consistency across labelers; low agreement indicates ambiguous tasks or poor guidelines.