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ARXIV:2604.04535 · LEARNING THEORY · SUBMITTED 07 APR · 20:14 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04535LEARNING THEORYSUBMITTED 07 APR · 20:14 UTCFRESHNESS UNKNOWNMark Braverman · Roi Livni · Yishay Mansour · Shay Moran · Kobbi Nissim · arXiv
This paper revisits a classical machine learning model to develop theoretical bounds for learning from equivalence queries under less adversarial conditions, with potential applications in model updates.
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Pain This paper revisits a classical machine learning model to develop theoretical bounds for learning from equivalence queries under less adversarial conditions, with potential applications in model updates.
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This paper revisits a classical machine learning model to develop theoretical bounds for learning from equivalence queries under less adversarial conditions, with potential applications in model updates. This differs from standard supervised learning frameworks,…
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…
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our analysis combines a game-theoretic view of symmetric adversaries with adaptive weighting methods and minimax arguments.
Learning Theory moved forward this cycle; last verified April 2026. Public score 2.0/10.
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This paper revisits a classical machine learning model to develop theoretical bounds for learning from equivalence queries under less adversarial conditions, with potential applications in model updates.
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10.48550/arXiv.2604.04535This paper revisits a classical machine learning model to develop theoretical bounds for learning from equivalence queries under less adversarial conditions, with potential applications in model updates.
Abstract
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 prior work assumes a \emph{full-information} setting, where the learner also observes the correct label of the counterexample, an assumption that is not always natural. We address these issues by restricting the environment to a broad class of less adversarial counterexample generators, which we call \emph{symmetric}. Informally, such generators choose counterexamples based only on the symmetric difference between the hypothesis and the target. This class captures natural mechanisms such as random counterexamples (Angluin and Dohrn, 2017; Bhatia, 2021; Chase, Freitag, and Reyzin, 2024), as well as generators that return the simplest counterexample according to a prescribed complexity measure. Within this framework, we study learning from equivalence queries under both full-information and bandit feedback. We obtain tight bounds on the number of learning rounds in both settings and highlight directions for future work. Our analysis combines a game-theoretic view of symmetric adversaries with adaptive weighting methods and minimax arguments.
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PROBLEM
This paper revisits a classical machine learning model to develop theoretical bounds for learning from equivalence queries under less adversarial conditions, with potential applications in model updates. This differs from standard supervised learning frameworks, which focus on l...
METHOD
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...
RESULT
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our analysis combines a game-theoretic view of symmetric adversaries with adaptive weighting methods and minimax arguments.
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Learning Theory moved forward this cycle; last verified April 2026. Public score 2.0/10.
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This paper revisits a classical machine learning model to develop theoretical bounds for learning from equivalence queries under less adversarial conditions, with potential applications in model updates. This differs from standard supervised learning frameworks, which focus on loss or regret minimization over a fixed sequence of prediction tasks.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 2.0/10 on the public viability pass. Our analysis combines a game-theoretic view of symmetric adversaries with adaptive weighting methods and minimax arguments.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Learning Theory moved forward this cycle; last verified April 2026. Public score 2.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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This paper revisits a classical machine learning model to develop theoretical bounds for learning from equivalence queries under less adversarial conditions, with potential applications in model updates.
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