6 citations · 12 across the 5 of their papers we have counts for
6 papers
Beyond Instance Discrimination: Relation-aware Contrastive Self-supervised Learning
Yifei Zhang, Chang Liu, Yu Zhou +3
Contrastive self-supervised learning (CSL) based on instance discrimination typically attracts positive samples while repelling negatives to learn representations with pre-defined…
Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning
Zihan Zhang, Yuhang Jiang, Yuan Zhou +1
In this paper, we study the episodic reinforcement learning (RL) problem modeled by finite-horizon Markov Decision Processes (MDPs) with constraint on the number of batches. The mu…
RBP-Pose: Residual Bounding Box Projection for Category-Level Pose Estimation
Ruida Zhang, Yan Di, Zhiqiang Lou +3
Category-level object pose estimation aims to predict the 6D pose as well as the 3D metric size of arbitrary objects from a known set of categories. Recent methods harness shape pr…
An Empirical Study on Distribution Shift Robustness From the Perspective of Pre-Training and Data Augmentation
Ziquan Liu, Yi Xu, Yuanhong Xu +5
The performance of machine learning models under distribution shift has been the focus of the community in recent years. Most of current methods have been proposed to improve the r…
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…
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…