Authors: Anastasis Kratsios, A. Martina Neuman, Gudmund Pammer
Published on: February 08, 2024
Impact Score: 8.3
Arxiv code: Arxiv:2402.05576
Summary
- What is new: The paper introduces a novel approach to machine learning that exploits the finite structures of digital computers to break the curse of dimensionality, offering dimension-free generalization bounds for specific models.
- Why this is important: Traditional machine learning models assume infinite input and output spaces, which doesn’t align with the realities of digital computation and its inherent limitations.
- What the research proposes: By leveraging the discrete nature of computational spaces, the research proposes new generalization bounds for kernel and deep ReLU MLP regressors that do not depend on dimensionality.
- Results: The study found that utilizing finite computational spaces and a new concentration of measure result leads to tighter generalization bounds, especially for realistic sample sizes.
Technical Details
Technological frameworks used: Metric embedding theory, optimal transport
Models used: Kernel regressors, deep ReLU MLP regressors
Data used: nan
Potential Impact
This research could impact a broad array of industries relying on machine learning, including tech companies focused on AI development, data analysis firms, and sectors where predictive modeling plays a key role.
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