24 citations · 29 across the 5 of their papers we have counts for
5 papers
MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning
Helbert Paat, Qing Lian, Weilong Yao +1
Advancements in deep learning-based 3D object detection necessitate the availability of large-scale datasets. However, this requirement introduces the challenge of manual annotatio…
Minimax Regret Optimization for Robust Machine Learning under Distribution Shift
Alekh Agarwal, Tong Zhang
In this paper, we consider learning scenarios where the learned model is evaluated under an unknown test distribution which potentially differs from the training distribution (i.e.…
Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets
Han Zhong, Wei Xiong, Jiyuan Tan +4
We study episodic two-player zero-sum Markov games (MGs) in the offline setting, where the goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset colle…
Exploring Geometric Consistency for Monocular 3D Object Detection
Qing Lian, Botao Ye, Ruijia Xu +2
This paper investigates the geometric consistency for monocular 3D object detection, which suffers from the ill-posed depth estimation. We first conduct a thorough analysis to reve…
Weakly Supervised Disentangled Generative Causal Representation Learning
Xinwei Shen, Furui Liu, Hanze Dong +3
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information. Unlike existing disentanglement methods that en…