4 citations · 11 across the 9 of their papers we have counts for
9 papers
Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Zachary D King, Maryam Khalid, Han Yu +9
Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (E…
GRAIL: A Benchmark for GRaph ActIve Learning in Dynamic Sensing Environments
Maryam Khalid, Akane Sano
Graph-based Active Learning (AL) leverages the structure of graphs to efficiently prioritize label queries, reducing labeling costs and user burden in applications like health moni…
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…
Exploration of LLMs, EEG, and behavioral data to measure and support attention and sleep
Akane Sano, Judith Amores, Mary Czerwinski
We explore the application of large language models (LLMs), pre-trained models with massive textual data for detecting and improving attention and sleep. We investigate the use of…
ECG Semantic Integrator (ESI): A Foundation ECG Model Pretrained with LLM-Enhanced Cardiological Text
Han Yu, Peikun Guo, Akane Sano
The utilization of deep learning on electrocardiogram (ECG) analysis has brought the advanced accuracy and efficiency of cardiac healthcare diagnostics. By leveraging the capabilit…
AdaWaveNet: Adaptive Wavelet Network for Time Series Analysis
Han Yu, Peikun Guo, Akane Sano
Time series data analysis is a critical component in various domains such as finance, healthcare, and meteorology. Despite the progress in deep learning for time series analysis, t…
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…