Optimal Mistake Bounds for Transductive Online Learning

Studies the value of seeing an unlabeled input sequence before online prediction. Matching bounds relate mistakes to Littlestone dimension and clarify the gap between transductive and standard online learning.

Studies the value of seeing an unlabeled input sequence before online prediction. Matching bounds relate mistakes to Littlestone dimension and clarify the gap between transductive and standard online learning.

When reading the original paper, identify its problem, inputs, outputs, and methodological assumptions.

Studies the value of seeing an unlabeled input sequence before online prediction. Matching bounds relate mistakes to Littlestone dimension and clarify the gap between transductive and standard online learning.

Check the baselines, datasets, evaluation protocol, and ablations in the original paper. This directory overview does not provide an independent evaluation or experimental results table.