Curriculum Learning

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.

Information that can identify an individual (directly or indirectly); requires careful handling and compliance.

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.

Selecting the most informative samples to label (e.g., uncertainty sampling) to reduce labeling cost.