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Found 4 papers, 0 papers with code
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Diffusion models have emerged as a robust framework for various generative tasks, such as image and audio synthesis, and have also demonstrated a remarkable ability to generate mixed-type tabular data comprising both continuous and discrete variables.
Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
To address these issues, we propose a bias mitigation method based on multi-task learning, utilizing the concept of Monte-Carlo dropout and Pareto optimality from multi-objective optimization.
Bias Reducing Multitask Learning on Mental Health Prediction
However, there is still a lack of standard in evaluating bias in such machine learning models in the field, which leads to challenges in providing reliable predictions and in addressing disparities.
Studying the Impact of Mood on Identifying Smartphone Users
This paper explores the identification of smartphone users when certain samples collected while the subject felt happy, upset or stressed were absent or present.
