4 papers
Improved Regret in Stochastic Decision-Theoretic Online Learning under Differential Privacy
Ruihan Wu, Yu-Xiang Wang
Hu and Mehta (2024) posed an open problem: what is the optimal instance-dependent rate for the stochastic decision-theoretic online learning (with actions and rounds) under…
Adaptive Estimation and Learning under Temporal Distribution Shift
Dheeraj Baby, Yifei Tang, Hieu Duy Nguyen +2
In this paper, we study the problem of estimation and learning under temporal distribution shift. Consider an observation sequence of length , which is a noisy realization of a…
Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
Dheeraj Baby, Boran Han, Shuai Zhang +3
We study the well-motivated problem of online distribution shift in which the data arrive in batches and the distribution of each batch can change arbitrarily over time. Since the…
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
Ruihan Wu, Siddhartha Datta, Yi Su +3
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challengi…