454 citations · 2.5k across the 90 of their papers we have counts for
9 papers · 1 filter
DATELINE: Deep Plackett-Luce Model with Uncertainty Measurements
Bo Han
The aggregation of k-ary preferences is a historical and important problem, since it has many real-world applications, such as peer grading, presidential elections and restaurant r…
Privacy-preserving Stochastic Gradual Learning
Bo Han, Ivor W. Tsang, Xiaokui Xiao +3
It is challenging for stochastic optimizations to handle large-scale sensitive data safely. Recently, Duchi et al. proposed private sampling strategy to solve privacy leakage in st…
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
Bo Han, Gang Niu, Xingrui Yu +4
Given data with noisy labels, over-parameterized deep networks can gradually memorize the data, and fit everything in the end. Although equipped with corrections for noisy labels,…
FasTer: Fast Tensor Completion with Nonconvex Regularization
Quanming Yao, James T Kwok, Bo Han
Low-rank tensor completion problem aims to recover a tensor from limited observations, which has many real-world applications. Due to the easy optimization, the convex overlapping…
Matrix Co-completion for Multi-label Classification with Missing Features and Labels
Miao Xu, Gang Niu, Bo Han +3
We consider a challenging multi-label classification problem where both feature matrix $\X$ and label matrix $\Y$ have missing entries. An existing method concatenated $\X$ and $\Y…
Beyond Unfolding: Exact Recovery of Latent Convex Tensor Decomposition under Reshuffling
Chao Li, Mohammad Emtiyaz Khan, Zhun Sun +4
Exact recovery of tensor decomposition (TD) methods is a desirable property in both unsupervised learning and scientific data analysis. The numerical defects of TD methods, however…