StrongerNASwithWeakerPredictors Appendix

We compare the effect of using different architecture encodings in in Table 2. We found when combined with CATE embedding [3], the performance of WeakNAS can be further improved, compared to WeakNAS baseline with adjacency matrix encoding used in [4]. Tofairly compare with BRP-NAS, we followthe exact same setting for our WeakNAS predictor, e.g., incorporating the same graph convolutional network (GCN) based predictor and using Top40 evaluation. As shown in Table 4, at 100 training samples, WeakNAS can achievecomparable performancetoBRP-NAS[5]. 2 Method #Train #Queries TestAcc.(%) We use uniform sampling due to a recent study [10] reveal that human-designed NAS search spaces usually contain a fair proportion of good models compared to random design spaces, for example, in Figure 9 of [10], it shows that in NASNet/Amoeba/PNAS/ENAS/DARTS search spaces, Top 5% of models only have a <1% performance gaptotheglobal optima.