However, categorical features present a unique challenge as they require embedding a typically vast vocabulary into a smaller vector space for further calculations.

Extended " T able 2" in submitted paper. Extended " T able 3" in submitted paper. We thank reviewers for their comments, and will carefully revise paper considering these comments. Q1 (R1): References and comparison with a baseline that learns embeddings only through a standard convnet. In Tab.2 of this rebuttal, the state-of-the-art method of AISI [7] also depends on We will give more details of these compared methods in paper for clarity.

B.1 BaselineGHN:GHN-1 GHNs were designed for NAS, which typically make strong assumptions about the choice of operations and their possible dimensions tomakesearch and learning feasible.

Parametric dimensionality reduction methods have gained prominence for their ability togeneralize tounseen datasets, anadvantage that traditional approaches typically lack. Despite their growing popularity, there remains a prevalent misconception among practitioners about the equivalence in performance between parametric and non-parametric methods. Here, we showthat these methods are not equivalent - parametric methods retain global structure but lose significant localdetails.

Note havethatd(z, w)=( y, z)w ifandonlyd(z, w)=( x, z)w. InProceedingsof the Twenty-sixth Annual ACMSymposiumon Principlesof Distributed Computing, PODC '07, pages 43-52, New York, NY, USA, 2007.