Online & Streaming Learning
Incremental updates, concept drift, regret bounds, and models that must learn from a moving distribution.
6concepts
67flashcards
43minutes of reading
- 01 Concept Drift and Adaptive Windows How a streaming learner decides which past data still applies, why a fixed window is wrong in both directions, and the detectors that size the window from the data.
- 02 Incremental Model Updates in Production What it takes to update a deployed model continuously, why the appeal is usually smaller than it appears, and the failure modes that make scheduled retraining the safer default.
- 03 Online Gradient Descent and FTRL-Proximal How projected online gradient descent earns its square-root regret, why follow-the-regularised-leader is the same algorithm written lazily, and how that rewrite let Google train sparse click-through models on billions of features.
- 04 Online Learning and Regret Bounds What it means to learn without assuming a data distribution, why regret against the best fixed predictor is the achievable guarantee, and what online convex optimisation delivers.
- 05 Prequential Evaluation Why a stream has no test set, how test-then-train uses every example twice without leaking, and the forgetting mechanisms that make the resulting accuracy curve informative.
- 06 Streaming Algorithms and Sketches How to compute statistics over a stream too large to store, the three canonical sketches and their guarantees, and the mergeability property that makes them work in distributed systems.