Research map / Label-efficient and robust learning / Recognition and long-tail

Active Learning as Nullspace Regulation: A Spectral Representation Perspective

NeurIPS 2026 · Label-efficient and robust learning › Recognition and long-tail · approach: Contrastive representation theory

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes reframing active learning as nullspace regulation, scoring unlabeled samples by the component of their features projected onto the orthogonal complement of the labeled set's feature span.

Problem

Existing active learning methods estimate sample value through empirical criteria such as prediction ambiguity, diversity, or distributional coverage, lacking a principled geometric account of which unlabeled samples are most valuable.

Approach

Treats the feature span of the current labeled set as the modeled subspace and its orthogonal complement as a residual subspace; the projected residual of an unlabeled sample's features is used to quantify its informativeness, giving a spectral-geometric selection criterion.

Closest papers by meaning

• Unsupervised Active Learning via Subspace Learning (AAAI 2021 · similarity 0.88)
• Spectral Alignment in Forward–Backward Representations via Temporal Abstraction (NeurIPS 2026 · similarity 0.86)
• On the Benefits of Active Data Collection in Operator Learning (ICML 2025 · similarity 0.86)
• A Unified Framework for Learning with Nonlinear Model Classes from Arbitrary Linear Samples (ICML 2024 · similarity 0.84)
• Improved Active Learning via Dependent Leverage Score Sampling (ICLR 2024 · similarity 0.84)
• Stochastic Encodings for Active Feature Acquisition (ICML 2025 · similarity 0.84)