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math.ST2026
Rolling Conformal Prediction in Sequential Model Training
Chen Cheng, Ruiting Liang, Rina Foygel Barber
We introduce Rolling Conformal Prediction (rolling-CP), a distribution-free predictive inference method for the setting of sequential model training. Specifically, given a data str…
math.ST2026
False positive control in time series coincidence detection
Ruiting Liang, Samuel Dyson, Rina Foygel Barber +1
We study the problem of coincidence detection in time series data, where we aim to determine whether the appearance of simultaneous or near-simultaneous events in two time series i…
math.ST2023
Algorithmic stability implies training-conditional coverage for distribution-free prediction methods
Ruiting Liang, Rina Foygel Barber
In a supervised learning problem, given a predicted value that is the output of some trained model, how can we quantify our uncertainty around this prediction? Distribution-free pr…