7 citations · 37 across the 15 of their papers we have counts for
6 papers · 1 filter
Robust Multi-Output Learning with Highly Incomplete Data via Restricted Boltzmann Machines
Giancarlo Fissore, Aurélien Decelle, Cyril Furtlehner +1
In a standard multi-output classification scenario, both features and labels of training data are partially observed. This challenging issue is widely witnessed due to sensor or da…
Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
Uchenna Akujuobi, Han Yufei, Qiannan Zhang +1
In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first le…
Prototypical Networks for Multi-Label Learning
Zhuo Yang, Yufei Han, Guoxian Yu +2
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative…
Recurrent Attention Walk for Semi-supervised Classification
Uchenna Akujuobi, Qiannan Zhang, Han Yufei +1
In this paper, we study the graph-based semi-supervised learning for classifying nodes in attributed networks, where the nodes and edges possess content information. Recent approac…
Robust Federated Training via Collaborative Machine Teaching using Trusted Instances
Yufei Han, Xiangliang Zhang
Federated learning performs distributed model training using local data hosted by agents. It shares only model parameter updates for iterative aggregation at the server. Although i…
Collaborative and Privacy-Preserving Machine Teaching via Consensus Optimization
Yufei Han, Yuzhe Ma, Christopher Gates +2
In this work, we define a collaborative and privacy-preserving machine teaching paradigm with multiple distributed teachers. We focus on consensus super teaching. It aims at organi…