4 papers
In-Context Learning Under Regime Change
Carson Dudley, Yutong Bi, Xiaofeng Liu +1
Non-stationary sequences arise naturally in control, forecasting, and decision-making. The data-generating process shifts at unknown times, and models must detect the change, disca…
When and How Unlabeled Data Provably Improve In-Context Learning
Yingcong Li, Xiangyu Chang, Muti Kara +3
Recent research shows that in-context learning (ICL) can be effective even when demonstrations have missing or incorrect labels. To shed light on this capability, we examine a cano…
MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEs
Xingsheng Chen, Regina Zhang, Bo Gao +5
Time series prediction plays a pivotal role across diverse domains such as finance, healthcare, energy systems, and environmental modeling. However, existing approaches often strug…
Identifying Trustworthiness Challenges in Deep Learning Models for Continental-Scale Water Quality Prediction
Xiaobo Xia, Xiaofeng Liu, Jiale Liu +5
Water quality is foundational to environmental sustainability, ecosystem resilience, and public health. Deep learning offers transformative potential for large-scale water quality…