Tag: concept
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An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity
An Adaptive Sampling Framework for Detecting Localized Concept Drift under Label Scarcity arXiv:2511.02452v1 Announce Type: new Abstract: Concept drift and label scarcity are two critical challenges limiting the robustness of predictive models in dynamic industrial environments. Existing drift detection methods often assume global shifts and rely on dense supervision, making them ill-suited for regression tasks…
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Concept activation vectors: a unifying view and adversarial attacks
Concept activation vectors: a unifying view and adversarial attacks arXiv:2509.22755v1 Announce Type: new Abstract: Concept Activation Vectors (CAVs) are a tool from explainable AI, offering a promising approach for understanding how human-understandable concepts are encoded in a model’s latent spaces. They are computed from hidden-layer activations of inputs belonging either to a concept class or…
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DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts
DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts arXiv:2507.18464v1 Announce Type: new Abstract: Learning from non-stationary data streams subject to concept drift requires models that can adapt on-the-fly while remaining resource-efficient. Existing adaptive ensemble methods often rely on coarse-grained adaptation mechanisms or simple voting schemes that fail to optimally leverage specialized knowledge. This…
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A Theory of Optimistically Universal Online Learnability for General Concept Classes
A Theory of Optimistically Universal Online Learnability for General Concept Classes arXiv:2501.08551v1 Announce Type: new Abstract: We provide a full characterization of the concept classes that are optimistically universally online learnable with ${0, 1}$ labels. The notion of optimistically universal online learning was defined in [Hanneke, 2021] in order to understand learnability under minimal assumptions.…