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CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Ramneet Kaur · Kaustubh Sridhar · Sangdon Park · Susmit Jha · Anirban Roy · Oleg Sokolsky · Insup Lee

Machine learning models are prone to make incorrect predictions on inputs that are far from the training distribution. This hinders their deployment in safety-critical domains such as autonomous vehicles and healthcare. A number of techniques have been proposed for out-of-distribution (OOD) detection on individual datapoints. But in many applications, the inputs to these models form a temporal sequence. Existing techniques for OOD detection in time-series either do not exploit temporal relationships in the sequence or do not provide any guarantees on detection. We develop a self-supervised learning approach, CODiT for OOD detection in time-series data with guarantees on detection. We illustrate CODiT's efficacy on autonomous driving vision datasets and physiological GAIT data. Our code is available at shorturl.at/fzR02.

Author Information

Ramneet Kaur (University of Pennsylvania)
Kaustubh Sridhar (University of Pennsylvania)
Sangdon Park (Georgia Institute of Technology)
Susmit Jha (SRI International)
Anirban Roy (SRI International)
Oleg Sokolsky (University of Penssylvania)
Insup Lee (University of Pennsylvania)

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