Real-world datasets are often biased with respect to key demographic factors such as race
and gender. Due to the latent nature of the underlying factors, detecting and mitigating bias …

A learned generative model often produces biased statistics relative to the underlying data
distribution. A standard technique to correct this bias is importance sampling, where …

We study relationships between spoken language and co-speech gestures in context of two
key challenges. First, distributions of text and gestures are inherently skewed making it …

Unsupervised image-to-image translation aims at learning the mapping from the source to
target domain without using paired images for training. An essential yet restrictive …

Topology optimization offers great potential to design periodic structures with desired
bandgap properties. This paper proposes a novel Subset Simulation (SS) based topology …

Generative adversarial network (GAN) has become one of the most important neural
network models for classical unsupervised machine learning. A variety of discriminator loss …

Research on tooth surface design based on deep neural networks has recently achieved
progress in terms of both accuracy and execution efficiency. However, unrealistic outputs …

Fairness in machine learning seeks to mitigate model bias against individuals based on
sensitive features such as sex or age, often caused by an uneven representation of the …

A fair classifier should ensure the benefit of people from different groups, while the group
information is often sensitive and unsuitable for model training. Therefore, learning a fair …

Unsupervised domain transfer is the task of transferring or translating samples from a source
distribution to a different target distribution. Current solutions unsupervised domain transfer …