5 papers
Towards Physics-informed Diffusion for Anomaly Detection in Trajectories
Arun Sharma, Mingzhou Yang, Majid Farhadloo +3
Given trajectory data, a domain-specific study area, and a user-defined threshold, we aim to find anomalous trajectories indicative of possible GPS spoofing (e.g., fake trajectory)…
Spatial Distribution-Shift Aware Knowledge-Guided Machine Learning
Arun Sharma, Majid Farhadloo, Mingzhou Yang +3
Given inputs of diverse soil characteristics and climate data gathered from various regions, we aimed to build a model to predict accurate land emissions. The problem is important…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Towards Kriging-informed Conditional Diffusion for Regional Sea-Level Data Downscaling
Subhankar Ghosh, Arun Sharma, Jayant Gupta +2
Given coarser-resolution projections from global climate models or satellite data, the downscaling problem aims to estimate finer-resolution regional climate data, capturing fine-s…
Conformal Prediction for Class-wise Coverage via Augmented Label Rank Calibration
Yuanjie Shi, Subhankar Ghosh, Taha Belkhouja +2
Conformal prediction (CP) is an emerging uncertainty quantification framework that allows us to construct a prediction set to cover the true label with a pre-specified marginal or…