17 citations · 86 across the 28 of their papers we have counts for
7 papers · 1 filter
Meta-Learning with Heterogeneous Tasks
Zhaofeng Si, Shu Hu, Kaiyi Ji +1
Meta-learning is a general approach to equip machine learning models with the ability to handle few-shot scenarios when dealing with many tasks. Most existing meta-learning methods…
Outlier Robust Adversarial Training
Shu Hu, Zhenhuan Yang, Xin Wang +2
Supervised learning models are challenged by the intrinsic complexities of training data such as outliers and minority subpopulations and intentional attacks at inference time with…
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation
Hao Chen, Peng Zheng, Xin Wang +5
As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent…
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…
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…
Sum of Ranked Range Loss for Supervised Learning
Shu Hu, Yiming Ying, Xin Wang +1
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual l…
Learning by Minimizing the Sum of Ranked Range
Shu Hu, Yiming Ying, Xin Wang +1
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output. Such cases occur in the aggregate loss, which combines individual l…