In real applications, interaction between machine learning models and domain experts is
critical; however, the classical machine learning paradigm that usually produces only a …

Abstract Machine learning techniques have gained attention in earthquake engineering for
their accurate predictions, but their opaque black-box models create ambiguity in the …

Machine learning is becoming increasingly prevalent for tackling challenges in earthquake
engineering and providing fairly reliable and accurate predictions. However, it is mostly …

This study introduces a novel methodology for enhancing the compressive strength of self-
compacting concrete (SCC) via the use of the Explainable Boosting Machine (EBM), a …

Advanced machine learning (ML) models are utilized for accurate shear strength prediction
of reinforced concrete beams (RCB), but their lack of interpretability makes it unclear how …

Apart from high accuracy, what interests many researchers and practitioners in real-life
tabular learning problems (eg, fraud detection and credit scoring) is uncovering hidden …

Background: Predicting cognition decline in patients with mild cognitive impairment (MCI) is
crucial for identifying high-risk individuals and implementing effective management. To …

There is a growing need for models that are interpretable and have reduced
energy/computational cost (eg, in health care analytics and federated learning). Examples of …

The interpretability of a machine learning system is crucial in situations where it involves
human-model interaction or affects the well-being of society. By making the decision process …

Abstract Machine learning has been increasingly deployed for myriad high-stakes decisions
that deeply impact people's lives. This is concerning, because not every model can be …