57 citations · 253 across the 23 of their papers we have counts for
32 papers
My Health Sensor, my Classifier: Adapting a Trained Classifier to Unlabeled End-User Data
Konstantinos Nikolaidis, Stein Kristiansen, Thomas Plagemann +8
In this work, we present an approach for unsupervised domain adaptation (DA) with the constraint, that the labeled source data are not directly available, and instead only access t…
Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda
Christian von der Weth, Ashraf Abdul, Shaojing Fan +1
Participation on social media platforms has many benefits but also poses substantial threats. Users often face an unintended loss of privacy, are bombarded with mis-/disinformation…
-Reference Transfer Learning for Saliency Prediction
Yan Luo, Yongkang Wong, Mohan S. Kankanhalli +1
Benefiting from deep learning research and large-scale datasets, saliency prediction has achieved significant success in the past decade. However, it still remains challenging to p…
Gender and Emotion Recognition from Implicit User Behavior Signals
Maneesh Bilalpur, Seyed Mostafa Kia, Mohan Kankanhalli +1
This work explores the utility of implicit behavioral cues, namely, Electroencephalogram (EEG) signals and eye movements for gender recognition (GR) and emotion recognition (ER) fr…
Robust Federated Recommendation System
Chen Chen, Jingfeng Zhang, Anthony K. H. Tung +2
Federated recommendation systems can provide good performance without collecting users' private data, making them attractive. However, they are susceptible to low-cost poisoning at…
Hierarchically Fair Federated Learning
Jingfeng Zhang, Cheng Li, Antonio Robles-Kelly +1
When the federated learning is adopted among competitive agents with siloed datasets, agents are self-interested and participate only if they are fairly rewarded. To encourage the…