3 papers
cs.LG2025
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…
cs.LG2025
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…
cs.LG2025
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…