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.

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas.

Several goal-oriented problems in the real-world can be naturally expressed as Stochastic Shortest Path problems (SSPs). However, the computational complexity of solving SSPs makes nding solutions to even moderately sized problems intractable. State-of-the-art SSP solvers are unable to learn generalized solutions or policies that would solve multiple problem instances with different object names and/or quantities. This paper presents an approach for learning Generalized Policy Automata (GPAs): non-deterministic partial policies that can be used to catalyze the solution process. GPAs are learned using relational, feature-based abstractions, which makes them applicable on broad classes of related problems with different object names and quantities. Theoretical analysis of this approach shows that it guarantees completeness and hierarchical optimality. Empirical analysis shows that this approach effectively learns broadly applicable policy knowledge in a few-shot fashion and signicantly outperforms state-of-the-art SSP solvers on test problems whose object counts are far greater than those used during training.