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Found 5 papers, 3 papers with code
Addressing Missing Sources with Adversarial Support-Matching
We investigate a scenario in which the absence of certain data is linked to the second level of a two-level hierarchy in the data.
Zero-shot Fairness with Invisible Demographics
In a statistical notion of algorithmic fairness, we partition individuals into groups based on some key demographic factors such as race and gender, and require that some statistics of a classifier be approximately equalized across those groups.
Null-sampling for Interpretable and Fair Representations
We propose to learn invariant representations, in the data domain, to achieve interpretability in algorithmic fairness.
Ranked #1 on
Image Classification
on CelebA 64x64
Tuning Fairness by Balancing Target Labels
The issue of fairness in machine learning models has recently attracted a lot of attention as ensuring it will ensure continued confidence of the general public in the deployment of machine learning systems.
Weakly Supervised One-Shot Detection with Attention Similarity Networks
Neural network models that are not conditioned on class identities were shown to facilitate knowledge transfer between classes and to be well-suited for one-shot learning tasks.
