3 papers
cs.LG2026
From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning
Lipeng Zu, Yu Qian, Shayok Chakraborty +1
Offline-to-online reinforcement learning (O2O RL) faces a central challenge between retaining offline conservatism and adapting to online feedback under distribution shift. This ch…
cs.LG2024
Theoretically Guaranteed Distribution Adaptable Learning
Chao Xu, Xijia Tang, Guoqing Liu +2
In many open environment applications, data are collected in the form of a stream, which exhibits an evolving distribution over time. How to design algorithms to track these evolvi…
cs.LG2024
Theory-inspired Label Shift Adaptation via Aligned Distribution Mixture
Ruidong Fan, Xiao Ouyang, Hong Tao +2
As a prominent challenge in addressing real-world issues within a dynamic environment, label shift, which refers to the learning setting where the source (training) and target (tes…