Exploring and interacting with the set of good sparse generalized additive models
In real applications, interaction between machine learning models and domain experts is
critical; however, the classical machine learning paradigm that usually produces only a …
critical; however, the classical machine learning paradigm that usually produces only a …
No more black-boxes: estimate deformation capacity of non-ductile RC shear walls based on generalized additive models
Abstract Machine learning techniques have gained attention in earthquake engineering for
their accurate predictions, but their opaque black-box models create ambiguity in the …
their accurate predictions, but their opaque black-box models create ambiguity in the …
Estimate deformation capacity of non-ductile rc shear walls using explainable boosting machine
ZT Deger, GT Kaya, JW Wallace - arXiv preprint arXiv:2301.04652, 2023 - arxiv.org
Machine learning is becoming increasingly prevalent for tackling challenges in earthquake
engineering and providing fairly reliable and accurate predictions. However, it is mostly …
engineering and providing fairly reliable and accurate predictions. However, it is mostly …
[HTML][HTML] Exploring the interrelationships between composition, rheology, and compressive strength of self-compacting concrete: An exploration of explainable boosting …
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 …
compacting concrete (SCC) via the use of the Explainable Boosting Machine (EBM), a …
[HTML][HTML] Proposing an inherently interpretable machine learning model for shear strength prediction of reinforced concrete beams with stirrups
J Shu, H Yu, G Liu, H Yang, W Guo, C Phoon… - Case Studies in …, 2024 - Elsevier
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 …
of reinforced concrete beams (RCB), but their lack of interpretability makes it unclear how …
Exploring accuracy and interpretability trade-off in tabular learning with novel attention-based models
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 …
tabular learning problems (eg, fraud detection and credit scoring) is uncovering hidden …
Explainable Machine Learning with Pairwise Interactions for Predicting Conversion from Mild Cognitive Impairment to Alzheimer's Disease Utilizing Multi-Modalities …
J Cai, W Hu, J Ma, A Si, S Chen, L Gong, Y Zhang… - Brain Sciences, 2023 - mdpi.com
Background: Predicting cognition decline in patients with mild cognitive impairment (MCI) is
crucial for identifying high-risk individuals and implementing effective management. To …
crucial for identifying high-risk individuals and implementing effective management. To …
Smoothly giving up: Robustness for simple models
T Sypherd, N Stromberg - … of The 26th International Conference on …, 2023 - par.nsf.gov
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 …
energy/computational cost (eg, in health care analytics and federated learning). Examples of …
Human-in-the-loop Machine Learning System via Model Interpretability
Z Chen - 2023 - search.proquest.com
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 …
human-model interaction or affects the well-being of society. By making the decision process …
[PDF][PDF] Interpretability and Multiplicity: a Path to Trustworthy Machine Learning
C Zhong - 2024 - dukespace.lib.duke.edu
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 …
that deeply impact people's lives. This is concerning, because not every model can be …