5 papers
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…
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…
Assumption-free stability for ranking problems
Ruiting Liang, Jake A. Soloff, Rina Foygel Barber +1
In this work, we consider ranking problems among a finite set of candidates: for instance, selecting the top- items among a larger list of candidates or obtaining the full ranki…
Conformal prediction after data-dependent model selection
Ruiting Liang, Wanrong Zhu, Rina Foygel Barber
Given a family of pretrained models and a hold-out set, how can we construct a valid conformal prediction set while selecting a model that minimizes the width of the set? If we use…
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…