7 papers
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…
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…
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…
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…
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…