31 citations · 58 across the 10 of their papers we have counts for
6 papers · 1 filter
Fault-Tolerant Evaluation for Sample-Efficient Model Performance Estimators
Zihan Zhu, Yanqiu Wu, Qiongkai Xu
In the era of Model-as-a-Service, organizations increasingly rely on third-party AI models for rapid deployment. However, the dynamic nature of emerging AI applications, the contin…
Quantum-Inspired Machine Learning: a Survey
Larry Huynh, Jin Hong, Ajmal Mian +3
Quantum-inspired Machine Learning (QiML) is a burgeoning field, receiving global attention from researchers for its potential to leverage principles of quantum mechanics within cla…
Spatio-temporal Incentives Optimization for Ride-hailing Services with Offline Deep Reinforcement Learning
Yanqiu Wu, Qingyang Li, Zhiwei Qin
A fundamental question in any peer-to-peer ride-sharing system is how to, both effectively and efficiently, meet the request of passengers to balance the supply and demand in real…
Aggressive Q-Learning with Ensembles: Achieving Both High Sample Efficiency and High Asymptotic Performance
Yanqiu Wu, Xinyue Chen, Che Wang +2
Recent advances in model-free deep reinforcement learning (DRL) show that simple model-free methods can be highly effective in challenging high-dimensional continuous control tasks…
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning
Xinyue Chen, Zijian Zhou, Zheng Wang +3
There has recently been a surge in research in batch Deep Reinforcement Learning (DRL), which aims for learning a high-performing policy from a given dataset without additional int…
Striving for Simplicity and Performance in Off-Policy DRL: Output Normalization and Non-Uniform Sampling
Che Wang, Yanqiu Wu, Quan Vuong +1
We aim to develop off-policy DRL algorithms that not only exceed state-of-the-art performance but are also simple and minimalistic. For standard continuous control benchmarks, Soft…