most citedShapelet-Based Counterfactual Explanations for Multivariate Time Series

2 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Predictive Insights into LGBTQ+ Minority Stress: A Transductive Exploration of Social Media Discourse

S. Chapagain, Y. Zhao, T. K. Rohleen +7

Individuals who identify as sexual and gender minorities, including lesbian, gay, bisexual, transgender, queer, and others (LGBTQ+) are more likely to experience poorer health than…

cs.LG2024

EXCON: Extreme Instance-based Contrastive Representation Learning of Severely Imbalanced Multivariate Time Series for Solar Flare Prediction

Onur Vural, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

In heliophysics research, predicting solar flares is crucial due to their potential to impact both space-based systems and Earth's infrastructure substantially. Magnetic field data…

cs.LG2024

M-CELS: Counterfactual Explanation for Multivariate Time Series Data Guided by Learned Saliency Maps

Peiyu Li, Omar Bahri, Soukaina Filali Boubrahimi +1

Over the past decade, multivariate time series classification has received great attention. Machine learning (ML) models for multivariate time series classification have made signi…

cs.LG2024

SeriesGAN: Time Series Generation via Adversarial and Autoregressive Learning

MohammadReza EskandariNasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Current Generative Adversarial Network (GAN)-based approaches for time series generation face challenges such as suboptimal convergence, information loss in embedding spaces, and i…

cs.LG20241 cited

Info-CELS: Informative Saliency Map Guided Counterfactual Explanation

Peiyu Li, Omar Bahri, Pouya Hosseinzadeh +2

As the demand for interpretable machine learning approaches continues to grow, there is an increasing necessity for human involvement in providing informative explanations for mode…

cs.LG20222 cited

Shapelet-Based Counterfactual Explanations for Multivariate Time Series

Omar Bahri, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi

As machine learning and deep learning models have become highly prevalent in a multitude of domains, the main reservation in their adoption for decision-making processes is their b…