Optimum-pursuing method for constrained optimization and reliability-based design optimization problems using Kriging model

This paper proposes a new active learning method named as optimum-pursuing method (OPM) from the viewpoint of optimization theory, which aims to provide an effective tool for solving constrained optimization and reliability-based design optimization (RBDO) problems with low computation cost. It uses the cheap Kriging metamodel to replace the...

ALK-PE: An efficient active learning Kriging approach for wave energy converter power matrix estimation

Wave energy is considered one of the most potential renewable energy. In the last two decades, many wave energy converters (WECs) have been designed to harvest energy from the ocean. Different power take-off systems are developed to maximize the power generation of WECs. However, the estimation of the power matrix of the WECs and annual power...

Knowledge- and ambiguity-aware robot learning from corrective and evaluative feedback

In order to deploy robots that could be adapted by non-expert users, interactive imitation learning (IIL) methods must be flexible regarding the interaction preferences of the teacher and avoid assumptions of perfect teachers (oracles), while considering they make mistakes influenced by diverse human factors. In this work, we propose an IIL...

Gender, prior knowledge, and the impact of a flipped linear algebra course for engineers over multiple years

Background: Research shows that active pedagogies could play an important role in achieving more equitable outcomes for diverse groups of students in Science, Technology, Engineering, and Mathematics (STEM). Although flipped classes are a popular active methodology, there is a lack of high-quality studies assessing their impact in...

To Actively Initialize Active Learning

Though much effort has been spent on designing new active learning algorithms, little attention has been paid to the initialization problem of active learning, i.e., how to find a set of labeled samples which contains at least one instance per category. This work identifies the initialization of active learning as a separate and novel...

On-the-fly construction of surrogate constitutive models for concurrent multiscale mechanical analysis through probabilistic machine learning

Concurrent multiscale finite element analysis (FE²) is a powerful approach for high-fidelity modeling of materials for which a suitable macroscopic constitutive model is not available. However, the extreme computational effort associated with computing a nested micromodel at every macroscopic integration point makes FE²...

A review and experimental analysis of active learning over crowdsourced data

Training data creation is increasingly a key bottleneck for developing machine learning, especially for deep learning systems. Active learning provides a cost-effective means for creating training data by selecting the most informative instances for labeling. Labels in real applications are often collected from crowdsourcing, which engages...

Reliability assessment with density scanned adaptive Kriging

Reliability assessment with adaptive Kriging has gained notoriety due to the Kriging capability of accurately replacing the performance function while performing as a self-improving function for learning procedures. Recent works on adaptive Kriging pursued to improve the efficiency of the active learning through the application of distinct...

Learning Interactively to Resolve Ambiguity in Reference Frame Selection

In Learning from Demonstrations, ambiguities can lead to bad generalization of the learned policy. This paper proposes a framework called Learning Interactively to Resolve Ambiguity (LIRA), that recognizes ambiguous situations, in which more than one action have similar probabilities, avoids a random action selection, and uses the human...

Nuclear discrepancy for single-shot batch active learning

Active learning algorithms propose what data should be labeled given a pool of unlabeled data. Instead of selecting randomly what data to annotate, active learning strategies aim to select data so as to get a good predictive model with as little labeled samples as possible. Single-shot batch active learners select all samples to be labeled in a...

A variance maximization criterion for active learning

Active learning aims to train a classifier as fast as possible with as few labels as possible. The core element in virtually any active learning strategy is the criterion that measures the usefulness of the unlabeled data based on which new points to be labeled are picked. We propose a novel approach which we refer to as maximizing variance...

A benchmark and comparison of active learning for logistic regression

Logistic regression is by far the most widely used classifier in real-world applications. In this paper, we benchmark the state-of-the-art active learning methods for logistic regression and discuss and illustrate their underlying characteristics. Experiments are carried out on three synthetic datasets and 44 real-world datasets, providing...