Causal Covariate Shift Correction using Fisher information penalty

Evolving feature densities across batches of training data bias cross-validation, making model selection and assessment unreliable (citesugiyama2012machine). This work takes a distributed density estimation angle to the training setting where data are temporally distributed. textitCausal Covariate Shift Correction (C3), accumulates knowledge about the data density of a training batch using Fisher Information, and using it to penalize the loss in all subsequent batches. The penalty improves accuracy by 12.9% over the full-dataset baseline, by 20.3% accuracy at maximum in batchwise and 5.9% at minimum in foldwise benchmarks.