Active Regression Method Improves Sample Complexity for Curve Fitting

This paper introduces a novel approach to active regression that significantly improves sample complexity for various curve fitting problems. The method achieves a constant factor approximation for linear regression with only O(d) labels, outperforming previous O(d log d) results. The research also extends to inductive settings, enabling generalization to new samples in continuous problems like polynomial regression, and offers improved techniques for non-linear sparse Fourier transforms. AI

RANK_REASON The cluster contains an academic paper detailing a new research methodology. [lever_c_demoted from research: ic=1 ai=1.0]

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• Xue Chen

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