The exponential use of artificial intelligence (AI) to solve and automated complex tasks has
catapulted its popularity generating some challenges that need to be addressed. While AI is …

In this work, we have proposed an adaptive-correction iterative ensemble smoother (ACIES)
to adaptively adjust the range of unknown variables, for improving the estimation accuracy …

Techniques are provided for selection of machine learning algorithms based on
performance predictions by trained algorithm-specific regressors. In an embodiment, a com …

Herein, horizontally scalable techniques efficiently configure machine learning algorithms
for optimal accuracy and without informed inputs. In an embodiment, for each particular …

In order to improve the accuracy of network security situation prediction and the
convergence speed of prediction algorithm, this paper proposes a combined prediction …

Feature engineering relies on domain knowledge and human intervention. To automate the
process of feature engineering, automated feature construction methods use deep neural …

Herein are techniques for automatic tuning of hyperparameters of machine learning
algorithms. System throughput is maximized by horizontally scaling and asynchronously …

The objective value of an ill-conditioned function may significantly change with a minor shift
of the argument in the search space. Ill-conditioned functions do not have at all or exhibit …

Algorithm selection and hyperparameter tuning are critical steps in both academic and
applied machine learning. On the other hand, these steps are becoming ever increasingly …

Efficient numerical optimization methods can improve performance and reduce the
environmental impact of computing in many applications. This work presents a proof-of …