24 citations · 50 across the 16 of their papers we have counts for
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cs.CV2021★ 24 cited
Improved Fine-Tuning by Better Leveraging Pre-Training Data
Ziquan Liu, Yi Xu, Yuanhong Xu +5
As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets. However, recent studie…
cs.LG2021★ 3 cited
Wasserstein Unsupervised Reinforcement Learning
Shuncheng He, Yuhang Jiang, Hongchang Zhang +2
Unsupervised reinforcement learning aims to train agents to learn a handful of policies or skills in environments without external reward. These pre-trained policies can accelerate…
cs.LG2021
Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP
Zihan Zhang, Jiaqi Yang, Xiangyang Ji +1
This paper presents new \emph{variance-aware} confidence sets for linear bandits and linear mixture Markov Decision Processes (MDPs). With the new confidence sets, we obtain the fo…