6 citations · 17 across the 15 of their papers we have counts for
5 papers · 1 filter
Contrastive Learning with Negative Sampling Correction
Lu Wang, Chao Du, Pu Zhao +8
As one of the most effective self-supervised representation learning methods, contrastive learning (CL) relies on multiple negative pairs to contrast against each positive pair. In…
COIN: Chance-Constrained Imitation Learning for Uncertainty-aware Adaptive Resource Oversubscription Policy
Lu Wang, Mayukh Das, Fangkai Yang +9
We address the challenge of learning safe and robust decision policies in presence of uncertainty in context of the real scientific problem of adaptive resource oversubscription to…
Counter-Empirical Attacking based on Adversarial Reinforcement Learning for Time-Relevant Scoring System
Xiangguo Sun, Hong Cheng, Hang Dong +3
Scoring systems are commonly seen for platforms in the era of big data. From credit scoring systems in financial services to membership scores in E-commerce shopping platforms, pla…
Robust Positive-Unlabeled Learning via Noise Negative Sample Self-correction
Zhangchi Zhu, Lu Wang, Pu Zhao +7
Learning from positive and unlabeled data is known as positive-unlabeled (PU) learning in literature and has attracted much attention in recent years. One common approach in PU lea…
Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning
Junjie Sheng, Lu Wang, Fangkai Yang +9
Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not al…