Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data

Feb-9-2026, 12:55:56 GMT–Neural Information Processing Systems

The premise of semi-supervised learning (SSL) is that combining labeled and unlabeled data yields significantly more accurate models.

artificial intelligence, machine learning, theorem 2, (20 more...)

Neural Information Processing Systems

Feb-9-2026, 12:55:56 GMT

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Country:
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)

Genre:
- Research Report > Experimental Study (0.93)

Technology:
- Information Technology > Artificial Intelligence
  - Machine Learning
    - Learning Graphical Models > Directed Networks
      - Bayesian Learning (0.67)
    - Statistical Learning (0.92)
    - Unsupervised or Indirectly Supervised Learning (1.00)
  - Representation & Reasoning > Uncertainty
    - Bayesian Inference (0.67)

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Semi-Supervised Sparse Gaussian Classification: Provable Benefits of Unlabeled Data

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