Distribution-free Deviation Bounds and The Role of Domain Knowledge in Learning via Model Selection with Cross-validation Risk Estimation
arXiv:2303.08777v3 Announce Type: replace-cross Abstract: Cross-validation is one of the most widely used tools for risk estimation and model selection in statistics and machine learning, yet its theoretical properties when embedded in a learning procedure remain insufficiently understood. This paper develops a general, distribution-free framework for learning via model selection with cross-validation risk estimation within classical statistical learning theory. We establish VC dimension-based deviation bounds for the entire learning pipeline, providing detailed proofs for both bounded and unb
This paper represents a refinement in the foundational theory of machine learning, an area that is under intense scholarly development due to the rapid advancements in AI applications.
Improved theoretical understanding of model selection and risk estimation strengthens the reliability and interpretability of AI systems, impacting their deployment across critical applications.
The development of a general, distribution-free framework for cross-validation within classical statistical learning theory provides more robust theoretical guarantees for AI model performance.
- · AI researchers
- · Machine learning engineers
- · Data scientists
- · Developers of mission-critical AI
- · Ad-hoc AI development
- · Systems with poorly understood risk profiles
More theoretically sound and reliable AI models will emerge from research incorporating these deviance bounds.
This improved theoretical grounding could accelerate the development and trust in autonomous AI agents by offering better performance guarantees.
The enhanced reliability of AI, stemming from such theoretical advances, may reduce barriers to broader adoption in highly regulated sectors.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG