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Early Concept Drift Detection via Prediction Uncertainty
Pengqian Lu, Jie Lu, Anjin Liu +1
Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in streaming data scenarios. Although…
Sharpness-Aware Cross-Domain Recommendation to Cold-Start Users
Guohang Zeng, Qian Zhang, Guangquan Zhang +1
Cross-Domain Recommendation (CDR) is a promising paradigm inspired by transfer learning to solve the cold-start problem in recommender systems. Existing state-of-the-art CDR method…
A Behavior-Aware Approach for Deep Reinforcement Learning in Non-stationary Environments without Known Change Points
Zihe Liu, Jie Lu, Guangquan Zhang +1
Deep reinforcement learning is used in various domains, but usually under the assumption that the environment has stationary conditions like transitions and state distributions. Wh…
A Neighbor-Searching Discrepancy-based Drift Detection Scheme for Learning Evolving Data
Feng Gu, Jie Lu, Zhen Fang +2
Uncertain changes in data streams present challenges for machine learning models to dynamically adapt and uphold performance in real-time. Particularly, classification boundary cha…