Learning in an Echo Chamber: Online Learning with Replay Adversary

As machine learning systems increasingly train on self-annotated data, they risk reinforcing errors and becoming echo chambers of their own beliefs. We model this phenomenon by introducing a learning-theoretic framework: Online Learning in the Replay Setting. In round t, the learner outputs a hypothesis hatht; the adversary then reveals either the true label f^ast(xt) or a replayed label hathi(xt) from an earlier round i < t. A mistake is counted only when the true label is shown, yet classical algorithms such as the SOA or the halving algorithm are easily misled by the replayed errors. We introduce the Extended Threshold dimension, ExThD(H), and prove matching upper and lower bounds that make ExThD(H) the exact measure of learnability in this model. A closure-based learner makes at most ExThD(H) mistakes against any adaptive adversary, and no algorithm can perform better. For stochastic adversaries, we prove a similar bound for every intersection-closed class. The replay setting is provably harder than the classical mistake bound setting: some classes have constant Littlestone dimension but arbitrarily large ExThD(H). Proper learning exhibits an even sharper separation: a class is properly learnable under replay if and only if it is (almost) intersection-closed. Otherwise, every proper learner suffers Omega(T) errors, whereas our improper algorithm still achieves the ExThD(H) bound. These results give the first tight analysis of learning against replay adversaries, based on new results for closure-type algorithms.