17 citations · 86 across the 28 of their papers we have counts for
7 papers · 1 filter
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
Shu Hu, George H. Chen
We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a use…
RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Control
Yanfei Xiang, Xin Wang, Shu Hu +4
Reinforcement learning is applied to solve actual complex tasks from high-dimensional, sensory inputs. The last decade has developed a long list of reinforcement learning algorithm…
Rank-based Decomposable Losses in Machine Learning: A Survey
Shu Hu, Xin Wang, Siwei Lyu
Recent works have revealed an essential paradigm in designing loss functions that differentiate individual losses vs. aggregate losses. The individual loss measures the quality of…
Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces
Hui Guo, Shu Hu, Xin Wang +2
AI-synthesized faces are visually challenging to discern from real ones. They have been used as profile images for fake social media accounts, which leads to high negative social i…
PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems
Shu Hu, Chun-Hao Liu, Jayanta Dutta +3
Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled. To propagate information from the labeled obje…
GAN-generated Faces Detection: A Survey and New Perspectives
Xin Wang, Hui Guo, Shu Hu +2
Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters…
Differentially Private SGDA for Minimax Problems
Zhenhuan Yang, Shu Hu, Yunwen Lei +3
Stochastic gradient descent ascent (SGDA) and its variants have been the workhorse for solving minimax problems. However, in contrast to the well-studied stochastic gradient descen…