In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

[EX Q]: Driven by the need for parallelizable hyperparameter optimization methods, this paper studies \emph{open loop} search methods: sequences that are predetermined and can be generated before a single configuration is evaluated. Examples include grid search, uniform random search, low discrepancy sequences, and other sampling distributions.
 In particular, we propose the use of $k$-determinantal point processes in  hyperparameter optimization via random search. Compared to conventional uniform random search where hyperparameter settings are sampled independently, a $k$-DPP promotes diversity.   We describe an approach that transforms hyperparameter search spaces for efficient use with a $k$-DPP. In addition, we introduce a novel Metropolis-Hastings algorithm which can sample from $k$-DPPs defined over spaces with a mixture of discrete and continuous dimensions. Our experiments show significant benefits over uniform random search  in realistic scenarios with a limited budget for training supervised learners, whether in serial or parallel.
[EX A]: Driven by the need for parallelizable, open-loop hyperparameter optimization methods, we propose the use of $k$-determinantal point processes in  hyperparameter optimization via random search.

[EX Q]: This paper proposes a method for efficient training of Q-function for continuous-state Markov Decision Processes (MDP), such that the traces of the resulting policies satisfy a Linear Temporal Logic (LTL) property. LTL, a modal logic, can express a wide range of time-dependent logical properties including safety and liveness. We convert the LTL property into a limit deterministic Buchi automaton with which a synchronized product MDP is constructed. The control policy is then synthesised by a reinforcement learning algorithm assuming that no prior knowledge is available from the MDP. The proposed method is evaluated in a numerical study to test the quality of the generated control policy and is compared against conventional methods for policy synthesis such as MDP abstraction (Voronoi quantizer) and approximate dynamic programming (fitted value iteration).
[EX A]: As safety is becoming a critical notion in machine learning we believe that this work can act as a foundation for a number of research directions such as safety-aware learning algorithms.

[EX Q]: Deep learning models have outperformed traditional methods in many fields such
 as natural language processing and computer vision. However, despite their
 tremendous success, the methods of designing optimal Convolutional Neural Networks
 (CNNs) are still based on heuristics or grid search. The resulting networks
 obtained using these techniques are often overparametrized with huge computational
 and memory requirements. This paper focuses on a structured, explainable
 approach towards optimal model design that maximizes accuracy while keeping
 computational costs tractable. We propose a single-shot analysis of a trained CNN
 that uses Principal Component Analysis (PCA) to determine the number of filters
 that are doing significant transformations per layer, without the need for retraining.
 It can be interpreted as identifying the dimensionality of the hypothesis space
 under consideration. The proposed technique also helps estimate an optimal number
 of layers by looking at the expansion of dimensions as the model gets deeper.
 This analysis can be used to design an optimal structure of a given network on
 a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate
 these techniques by optimizing VGG and AlexNet networks on CIFAR-10,
 CIFAR-100 and ImageNet datasets.
[EX A]:
We present a single shot analysis of a trained neural network to remove redundancy and identify optimal network structure

