7 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…
Leave a Window Out: Modifying the Jackknife for Predictive Inference in Time Series
Hanyang Jiang, Rina Foygel Barber, Ashwin Pananjady +1
Conformal prediction methods enjoy strong theoretical and empirical predictive inference performance, provided the data is exchangeable and is treated symmetrically during training…
Is Memorization Helpful or Harmful? Prior Information Sets the Threshold
Chen Cheng, Rina Foygel Barber
We examine the connection between training error and generalization error for arbitrary estimating procedures, working in an overparameterized linear model under general priors in…
Concentration Inequalities for Exchangeable Tensors and Matrix-valued Data
Chen Cheng, Rina Foygel Barber
We study concentration inequalities for structured weighted sums of random data, including (i) tensor inner products and (ii) sequential matrix sums. We are interested in tail boun…
Predictive inference for time series: why is split conformal effective despite temporal dependence?
Rina Foygel Barber, Ashwin Pananjady
We consider the problem of uncertainty quantification for prediction in a time series: if we use past data to forecast the next time point, can we provide valid prediction interval…
Unifying Different Theories of Conformal Prediction
Rina Foygel Barber, Ryan J. Tibshirani
This paper presents a unified framework for understanding the methodology and theory behind several different methods in the conformal prediction literature, which includes standar…