OpenAI Blog
May 25, 2016
Introduction to Semi-Supervised Learning
A primer about Semi-Supervised Learning, the approaches taken with different algorithms and the limitations of using unlabelled data. The post Introduction to Semi-Supervised Learning appeared first on Towards Data Science .
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arXiv Machine Learning
Jun 2
Semi-Supervised Learning with Noisy Proxy Covariates: Generalization Bounds and Distribution Regression
arXiv:2606. 00512v1 Announce Type: new Abstract: In many modern machine learning pipelines, abundant pretrained representations serve as noisy proxy covariates, while task-specific labels remain scarce.
Towards Data Science
Jul 27
Reducing Human Annotation with ML Active Learning
In a world where human time is expensive, learn how to use it only when really necessary The post Reducing Human Annotation with ML Active Learning appeared first on Towards Data Science .
arXiv Machine Learning
Jun 24
Relatively Smart: A New Approach for Instance-Optimal Learning
arXiv:2603. 01346v2 Announce Type: replace Abstract: We revisit the framework of Smart PAC learning, which seeks supervised learners which compete with semi-supervised learners that are provided full knowledge of the marginal distribution on unlabeled data.
OpenAI Blog
Aug 18, 2016
Machine Learning Unconference
The latest information about the Unconference is now available at the Unconference wiki, which will be periodically updated with more information for attendees.
arXiv Machine Learning
Aug 5
To Describe or Construct Statistical Learning Models Using the Category-theoretical Language
arXiv:2608. 03706v1 Announce Type: new Abstract: Statistical learning is a fascinating field that has long been the mainstream of machine learning/artificial intelligence.
arXiv AI
Jul 15
Generalized Distribution-Free Semi-Supervised Learning with Risk Rewrite
arXiv:2607. 11947v1 Announce Type: cross Abstract: Typical semi-supervised learning (SSL) methods rely on distributional assumptions, and their performance degrades when these are violated.
arXiv Machine Learning
Jun 2
Semi-Supervised Noise Adaptation: Transferring Knowledge from Noise Domain
arXiv:2606. 00558v1 Announce Type: new Abstract: Transfer learning aims to facilitate the learning of a target domain by transferring knowledge from a source domain.
Hugging Face Blog
May 17, 2022
Machine Learning Experts - Sasha Luccioni
Hugging Face Trending Papers
Aug 4
To Describe or Construct Statistical Learning Models Using the Category-theoretical Language
Statistical learning is a fascinating field that has long been the mainstream of machine learning/artificial intelligence. A large number of results have been produced which can be widely applied to real-world problems.
arXiv Machine Learning
Jul 21
Semi-Supervised Conditional Diffusion via Label Augmentation
arXiv:2607. 16685v1 Announce Type: cross Abstract: Conditional diffusion models have become a powerful and flexible framework for learning complex conditional distributions from labeled data.
arXiv AI
Sep 15
Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning
arXiv:2609.14451v1 Announce Type: cross Abstract: Modern semi-supervised learning (SSL) couples pseudo-label generation and classifier training, using the classifier's own confidence to select the ps...