Adaptive estimation and learning under temporal distribution shift

In this paper, we study the problem of estimation and learning under temporal distribution shift. Consider an observation sequence of length n, which is a noisy realization of a time-varying ground-truth sequence. Our focus is to develop methods to estimate the ground-truth at the final time-step while providing sharp point-wise estimation error rates. We show that, without prior knowledge on the level of temporal shift, a wavelet soft-thresholding estimator provides an optimal estimation error bound for the ground-truth. Our proposed estimation method generalizes existing researches Mazzetto and Upfal (2023) by establishing a connection between the sequence’s non-stationarity level and the sparsity in the wavelet-transformed domain. Our theoretical findings are validated by numerical experiments. Additionally, we applied the estimator to derive sparsity-aware excess risk bounds for binary classification under distribution shift and to develop computationally efficient training objectives. As a final contribution, we draw parallels between our results and the classical signal processing problem of total variation denoising (Mammen and van de Geer, 1997; Tibshirani, 2014a), uncovering novel optimal algorithms for such task.