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arXiv Machine Learning By Julian Asilis, Shaddin Dughmi, Chirag Pabbaraju

When Clean Data Hurts: Learning with Monotone Corruptions Beyond Binary Classification

Read the original on arXiv Machine Learning →

The paper investigates learning with monotone adversarial corruptions, extending previous binary classification results to multiclass and partial binary settings. It shows that even a small number of strategically inserted corrupted examples can render a learnable multiclass problem with DS dimension 2 completely unlearnable, and provides matching upper bounds when the adversary’s budget is sublinear. The work also demonstrates that classic error rates remain attainable under bounded or limited‑view adversaries.

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.

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