2 citations · 2 across the 15 of their papers we have counts for
7 papers · 1 filter
ReliaGate: Reliability Routing for Low-Stakes Wearable Stress Prediction
Jaden Moon, Yu Wu, Arvind Pillai +1
We study when a wearable stress system should surface a prediction rather than change it. In low-stakes reflection and summary settings, aggregate accuracy is insufficient because…
ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Models
Sotirios Vavaroutas, Yu Yvonne Wu, Ali Etemad +1
Data samples used for training often differ from those encountered during fine-tuning and deployment, and while ML models show promise, their performance remains limited when only…
Wearable Foundation Models Should Go Beyond Static Encoders
Yu Yvonne Wu, Yuwei Zhang, Hyungjun Yoon +8
Wearable foundation models (WFMs), trained on large volumes of data collected by affordable, always-on devices, have demonstrated strong performance on short-term, well-defined hea…
Rethinking Large Language Models For Irregular Time Series Classification In Critical Care
Feixiang Zheng, Yu Wu, Cecilia Mascolo +1
Time series data from the Intensive Care Unit (ICU) provides critical information for patient monitoring. While recent advancements in applying Large Language Models (LLMs) to time…
AdaNODEs: Test Time Adaptation for Time Series Forecasting Using Neural ODEs
Ting Dang, Soumyajit Chatterjee, Hong Jia +3
Test time adaptation (TTA) has emerged as a promising solution to adapt pre-trained models to new, unseen data distributions using unlabeled target domain data. However, most TTA m…
StatioCL: Contrastive Learning for Time Series via Non-Stationary and Temporal Contrast
Yu Wu, Ting Dang, Dimitris Spathis +2
Contrastive learning (CL) has emerged as a promising approach for representation learning in time series data by embedding similar pairs closely while distancing dissimilar ones. H…