The objectives are often black-box functions whose evaluations are time-consuming and costly.

Computing the gradient of model hyperparameters, i.e., hypergradient, enables a

Unlike AC, DAC can dynamically adjust the algorithm's configuration during

We first learn the decoder: a differentiable surrogate that approximates the many-to-one physical realization process.

Many of the recent triumphs in machine learning are dependent on well-tuned hyperparameters. This is particularly prominent in reinforcement learning (RL) where a small change in the configuration can lead to failure. Despite the importance of tuning hyperparameters, it remains expensive and is often done in a naive and laborious way. A recent solution to this problem is Population Based Training (PBT) which updates both weights and hyperparameters in a single training run of a population of agents. PBT has been shown to be particularly effective in RL, leading to widespread use in the field. However, PBT lacks theoretical guarantees since it relies on random heuristics to explore the hyperparameter space.

A recursive strategy is able to efficiently calculate the contributions of particular substring, pre-calculating contributions of the smaller sub-strings contained within the target string. Context-free grammars (CFG) are 4-tuples G = ( V, Σ,R,S), consisting of: a set of non-terminal symbols V, a set of terminal symbols Σ (also known as an alphabet), a set of production rules R, a non-terminal starting symbol S from which all strings are generated. The CFG for the symbolic regression task of Section 5.3 is given by the following rules: S S '+' T S S ' ' T S S '/' T S T T '(' S ')' T ' sin (' S ')' T'exp (' S ')' T'x' T '1' T '2' T '3', We now provide implementation details for our GA acquisition function optimizers. The GA begins with a randomly sampled population and ends once the best string in the population stops improving between iterations (Algorithm 1). Although seemingly simple tasks, our synthetic string optimization tasks of Section 5.1 are deceptively We now provide comprehensive experimental results across the synthetic string optimization tasks.

Our approach instead builds a powerful Gaussian process surrogate model based on string kernels, naturally supporting variable length inputs, and performs efficient acquisition function maximization for spaces with syntactical constraints.

However, existing graph neural networks (GNNs) fail to capture such an important property. To make GNN aware of automorphic equivalence, we first introduce a localized variant of this concept -- ego-centered automorphic equivalence (Ego-AE). Then, we design a novel variant of GNN, i.e., GRAPE, that uses learnable AE-aware aggregators to explicitly differentiate the Ego-AE