In recent years, soft computing methods have generated a large research interest. The synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. A particular evolutionary algorithm (EA) is differential evolution (DE). As for any EA, DE algorithm also requires parameters tuning to achieve desirable performance. In this paper tuning the perturbation factor vector of DE ...

Fine-tuning pre-trained models has been ubiquitously proven to be effective in a wide range of NLP tasks. However, fine-tuning the whole model is parameter inefficient as it always yields an entirely new for each task. Currently, many research works propose only fine-tune small portion parameters while keeping most shared across different These methods achieve surprisingly good performance and ...

In this paper, we address the challenging problem of selecting tuning parameters for high-dimensional sparse regression. We propose a simple and computationally efficient method, called path thresholding (PaTh), that transforms any tuning parameter-dependent sparse regression algorithm into an asymptotically tuning-free sparse regression algorithm. More specifically, we prove that, as the probl...

In recent years, soft computing methods have generated a large research interest. The synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. A particular evolutionary algorithm (EA) is differential evolution (DE). As for any EA, DE algorithm also requires parameters tuning to achieve desirable performance. In this paper tuning the perturbation factor vector of DE ...

10 The adaptive lasso is a commonly applied penalty for variable selection in regression mod11 eling. Like all penalties though, its performance depends critically on the choice of tuning 12 parameter. One method for choosing the tuning parameter is via information criteria, such as 13 those based on AIC and BIC. However, these criteria were developed for use with unpenal14 ized maximum likelih...

We investigate an automatic and dynamic parameter tuning of a statistical method for detecting anomalies in network traffic (this tuning is referred to as parameter learning) towards real-time detection. The main idea behind the dynamic tuning is to predict an appropriate parameter for upcoming traffic by considering the detection results of past t traces of traffic. The t is referred to as the...