2 citations · 3 across the 7 of their papers we have counts for
4 papers · 1 filter
Forced Exploration in Bandit Problems
Han Qi, Fei Guo, Li Zhu
The multi-armed bandit(MAB) is a classical sequential decision problem. Most work requires assumptions about the reward distribution (e.g., bounded), while practitioners may have d…
Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CPC)
Fei Guo, Li Zhu, YiKang Wang +1
Recently, few-shot action recognition has significantly progressed by learning the feature discriminability and designing suitable comparison methods. Still, there are the followin…
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets
Frederic Wang, Han Qi, Alfredo De Goyeneche +3
Although deep learning (DL) methods are powerful for solving inverse problems, their reliance on high-quality training data is a major hurdle. This is significant in high-dimension…
Discounted Thompson Sampling for Non-Stationary Bandit Problems
Han Qi, Yue Wang, Li Zhu
Non-stationary multi-armed bandit (NS-MAB) problems have recently received significant attention. NS-MAB are typically modelled in two scenarios: abruptly changing, where reward di…