This paper delves into the complexities of online linear regression, particularly in high-dimensional settings where only a subset of features are truly predictive. Researchers have explored 'feature priming' techniques to improve efficiency by estimating feature weights and refitting models. However, this work demonstrates a fundamental limitation in existing methods, showing that nuisance interpolation can cause these rules to underweight crucial predictive coordinates. The paper provides theoretical lower bounds on regret for common feature priming rules and offers a matching upper bound for a specific univariate case, suggesting avenues for future research on multivariate frontiers. AI
IMPACT This research provides theoretical insights into the limitations of feature priming in high-dimensional online linear regression, potentially influencing future algorithm design for AI systems that rely on efficient feature selection.
RANK_REASON Academic paper on a theoretical machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]
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