1 code implementation • 25 Jan 2021 • Shujian Yu, Francesco Alesiani, Xi Yu, Robert Jenssen, Jose C. Principe
Measuring the dependence of data plays a central role in statistics and machine learning.
1 code implementation • 31 Jan 2021 • Xi Yu, Shujian Yu, Jose C. Principe
We introduce the matrix-based Renyi's $\alpha$-order entropy functional to parameterize Tishby et al. information bottleneck (IB) principle with a neural network.
no code implementations • 29 Apr 2021 • Shuang Zhang, Liyao Xiang, Xi Yu, Pengzhi Chu, Yingqi Chen, Chen Cen, Li Wang
Real-world data is usually segmented by attributes and distributed across different parties.
1 code implementation • 12 Oct 2021 • Francesco Alesiani, Shujian Yu, Xi Yu
By learning minimum sufficient representations from training data, the information bottleneck (IB) approach has demonstrated its effectiveness to improve generalization in different AI applications.
Adversarial Robustness Out of Distribution (OOD) Detection +1
1 code implementation • NeurIPS 2021 • Xiang Gu, Xi Yu, Yan Yang, Jian Sun, Zongben Xu
To tackle the challenge of negative domain transfer, we propose a novel Adversarial Reweighting (AR) approach that adversarially learns the weights of source domain data to align the source and target domain distributions, and the transferable deep recognition network is learned on the reweighted source domain data.
Ranked #1 on Partial Domain Adaptation on DomainNet
no code implementations • 8 Sep 2023 • Xi Yu, Huan-Hsin Tseng, Shinjae Yoo, Haibin Ling, Yuewei Lin
Specifically, we first propose an information theory inspired loss function to ensure the disentangled class-relevant features contain sufficient class label information and the other disentangled auxiliary feature has sufficient domain information.