178 citations · 520 across the 20 of their papers we have counts for
20 papers
Towards Inference Efficient Deep Ensemble Learning
Ziyue Li, Kan Ren, Yifan Yang +3
Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e.g., can be up to 2048X in large-scale ensemble tasks. However,…
Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution
Wang Lu, Jindong Wang, Yidong Wang +1
The distribution shifts between training and test data typically undermine the performance of models. In recent years, lots of work pays attention to domain generalization (DG) whe…
Bootstrapped Transformer for Offline Reinforcement Learning
Kerong Wang, Hanye Zhao, Xufang Luo +3
Offline reinforcement learning (RL) aims at learning policies from previously collected static trajectory data without interacting with the real environment. Recent works provide a…
Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemble
Zhengyu Yang, Kan Ren, Xufang Luo +5
It is challenging for reinforcement learning (RL) algorithms to succeed in real-world applications like financial trading and logistic system due to the noisy observation and envir…
Domain Generalization using Pretrained Models without Fine-tuning
Ziyue Li, Kan Ren, Xinyang Jiang +3
Fine-tuning pretrained models is a common practice in domain generalization (DG) tasks. However, fine-tuning is usually computationally expensive due to the ever-growing size of pr…
Towards Generating Real-World Time Series Data
Hengzhi Pei, Kan Ren, Yuqing Yang +3
Time series data generation has drawn increasing attention in recent years. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usua…