Surprises in Proper Positive-Only Learning
Binary classification from positive-only samples is a variant of PAC learning in which the learner receives i.i.d. samples from the positive region of an unknown target concept, but is evaluated under the original distribution (which places mass on both positive and negative regions). This model dates back to Natarajan [1987, STOC], and the characterization of improper learning is well-known -- it even appears in textbooks. The characterization of proper positive-only learning, however, has long remained open. In this work, we revisit and settle this question: a concept class is properly learnable from positive-only samples if and only if it has finite VC dimension and satisfies a new com...
| Authors | Shai Ben-David, Farnam Mansouri, Anay Mehrotra et al. |
|---|---|
| Primary category | stat.ML |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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