This research paper delves into the theoretical aspects of interpolation and aggregation within regression models. The authors introduce the concept of $\gamma$-graph dimension as a key factor for understanding learnability across various aggregation techniques. They demonstrate that a simple median-based aggregation of three interpolating hypotheses achieves optimal performance, surpassing traditional proper learning methods. The paper also highlights that certain hypothesis classes can only be learned through infinite aggregation or non-interpolating rules, indicating limitations of finite interpolating aggregations. AI
RANK_REASON This is a theoretical research paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]
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