Modern applications—from ad platforms calibrating click-through predictions to polling systems incorporating responses to ML algorithms adapting fairness thresholds—share a common challenge: maintaining calibrated weights on live data streams. To address streaming data, we recast raking as a streaming convex optimization problem: minimize the squared error between current weighted

Gradient descent commits to a single direction at each step based on the local gradient. This myopic approach can be suboptimal when gradients are noisy, local geometry is misleading, or the loss landscape has multiple competing descent directions. The algorithm makes irrevocable decisions based on local information, potentially missing better

Shuffle your training data, and XGBoost might give you a different model. Even when you keep features, hyperparameters, and random_state fixed. This behavior violates what most practitioners reasonably expect: that models should be invariant to row permutation. And can lead to silent drift, flaky tests, and spurious alerts. This,