Learning from Equivalence Queries, Revisited
Modern machine learning systems, such as generative models and recommendation systems, often evolve through a cycle of deployment, user interaction, and periodic model updates. This differs from standard supervised learning frameworks, which focus on loss or regret minimization over a fixed sequence of prediction tasks. Motivated by this setting, we revisit the classical model of learning from equivalence queries, introduced by Angluin (1988). In this model, a learner repeatedly proposes hypotheses and, when a deployed hypothesis is inadequate, receives a counterexample. Under fully adversarial counterexample generation, however, the model can be overly pessimistic. In addition, most prio...
| Authors | Mark Braverman, Roi Livni, Yishay Mansour et al. |
|---|---|
| Primary category | cs.LG |
| Published | 6 April 2026 |
| Last updated at source | 25 June 2026 |
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