Adaptive and oblivious statistical adversaries are equivalent
Read the original on arXiv Machine Learning →The paper resolves a key question in statistical learning under adversarial corruption by showing that sample‑adaptive and sample‑oblivious adversaries are equivalent up to polynomial factors in the sample size for all corruption types. It proves that any algorithm that succeeds against a sample‑oblivious adversary can be transformed into one that succeeds against the corresponding sample‑adaptive adversary by requesting a polynomially larger sample and running the original algorithm on a random subsample. The construction preserves computational efficiency and requires only a simple modification of the algorithm.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.