Bayes-Sufficient Representations in Supervised Learning
📰 ArXiv cs.AI
Learn how Bayes-sufficient representations can improve supervised learning by preserving relevant information for prediction
Action Steps
- Define a representation as Bayes-sufficient for a joint distribution and loss
- Determine the target information loss-dependent for a given supervised decision problem
- Implement a Bayes-optimal action rule using a prediction head and the Bayes-sufficient representation
- Evaluate the performance of the Bayes-sufficient representation on a supervised learning task
- Compare the results with other representation learning methods to assess the benefits of Bayes-sufficient representations
Who Needs to Know This
Machine learning engineers and researchers can benefit from understanding Bayes-sufficient representations to develop more effective supervised learning models
Key Insight
💡 Bayes-sufficient representations preserve the relevant information for prediction in a supervised learning task
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🤖 Improve supervised learning with Bayes-sufficient representations! 📊
Full Article
Title: Bayes-Sufficient Representations in Supervised Learning
Abstract:
arXiv:2606.04045v1 Announce Type: cross Abstract: Representation learning is often described as preserving the information in an input that is relevant for prediction. This work asks what relevance means for a fixed supervised decision problem. A representation is defined to be Bayes-sufficient for a joint distribution and loss if some prediction head can use it to implement a Bayes-optimal action rule. This makes the target information loss-dependent. In the almost-surely unique Bayes-action ca
Abstract:
arXiv:2606.04045v1 Announce Type: cross Abstract: Representation learning is often described as preserving the information in an input that is relevant for prediction. This work asks what relevance means for a fixed supervised decision problem. A representation is defined to be Bayes-sufficient for a joint distribution and loss if some prediction head can use it to implement a Bayes-optimal action rule. This makes the target information loss-dependent. In the almost-surely unique Bayes-action ca
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