1 citations · 3 across the 6 of their papers we have counts for
5 papers · 1 filter
Uncovering Bias Paths with LLM-guided Causal Discovery: An Active Learning and Dynamic Scoring Approach
Khadija Zanna, Akane Sano
Ensuring fairness in machine learning requires understanding how sensitive attributes like race or gender causally influence outcomes. Existing causal discovery (CD) methods often…
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring
Khadija Zanna, Akane Sano
Causal discovery (CD) plays a pivotal role in numerous scientific fields by clarifying the causal relationships that underlie phenomena observed in diverse disciplines. Despite sig…
Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models
Zeyu Yang, Han Yu, Peikun Guo +3
Diffusion models have emerged as a robust framework for various generative tasks, including tabular data synthesis. However, current tabular diffusion models tend to inherit bias i…
Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
Khadija Zanna, Akane Sano
Bias originates from both data and algorithmic design, often exacerbated by traditional fairness methods that fail to address the subtle impacts of protected attributes. This study…
Bias Reducing Multitask Learning on Mental Health Prediction
Khadija Zanna, Kusha Sridhar, Han Yu +1
There has been an increase in research in developing machine learning models for mental health detection or prediction in recent years due to increased mental health issues in soci…