506 citations · 1.3k across the 52 of their papers we have counts for
10 papers · 1 filter
Nearly Minimax Optimal Reward-free Reinforcement Learning
Zihan Zhang, Simon S. Du, Xiangyang Ji
We study the reward-free reinforcement learning framework, which is particularly suitable for batch reinforcement learning and scenarios where one needs policies for multiple rewar…
Is Reinforcement Learning More Difficult Than Bandits? A Near-optimal Algorithm Escaping the Curse of Horizon
Zihan Zhang, Xiangyang Ji, Simon S. Du
Episodic reinforcement learning and contextual bandits are two widely studied sequential decision-making problems. Episodic reinforcement learning generalizes contextual bandits an…
Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations
Zhigang Li, Yinlin Hu, Mathieu Salzmann +1
While much progress has been made in 6-DoF object pose estimation from a single RGB image, the current leading approaches heavily rely on real-annotation data. As such, they remain…
Depth image denoising using nuclear norm and learning graph model
Chenggang Yan, Zhisheng Li, Yongbing Zhang +3
The depth images denoising are increasingly becoming the hot research topic nowadays because they reflect the three-dimensional (3D) scene and can be applied in various fields of c…
Domain Contrast for Domain Adaptive Object Detection
Feng Liu, Xiaoxong Zhang, Fang Wan +2
We present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors. DC is deduced from the error bound minimiz…
Skill Discovery of Coordination in Multi-agent Reinforcement Learning
Shuncheng He, Jianzhun Shao, Xiangyang Ji
Unsupervised skill discovery drives intelligent agents to explore the unknown environment without task-specific reward signal, and the agents acquire various skills which may be us…
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity
Zihan Zhang, Yuan Zhou, Xiangyang Ji
In this paper we consider the problem of learning an -optimal policy for a discounted Markov Decision Process (MDP). Given an MDP with states, actions, the discount fact…
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition
Zihan Zhang, Yuan Zhou, Xiangyang Ji
We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with states, actions, and episode length . We prop…
Self6D: Self-Supervised Monocular 6D Object Pose Estimation
Gu Wang, Fabian Manhardt, Jianzhun Shao +3
6D object pose estimation is a fundamental problem in computer vision. Convolutional Neural Networks (CNNs) have recently proven to be capable of predicting reliable 6D pose estima…
Rapid Whole Slide Imaging via Learning-based Two-shot Virtual Autofocusing
Qiang Li, Xianming Liu, Kaige Han +3
Whole slide imaging (WSI) is an emerging technology for digital pathology. The process of autofocusing is the main influence of the performance of WSI. Traditional autofocusing met…