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Found 7 papers, 1 papers with code
Towards Poisoning Fair Representations
Fair machine learning seeks to mitigate model prediction bias against certain demographic subgroups such as elder and female.
SimFair: A Unified Framework for Fairness-Aware Multi-Label Classification
This new framework utilizes data that have similar labels when estimating fairness on a particular label group for better stability, and can unify DP and EOp.
Optimization for Amortized Inverse Problems
Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations.
Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks
Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of supervised learning applications.
PANOM: Automatic Hyper-parameter Tuning for Inverse Problems
Automated hyper-parameter tuning for unsupervised learning, including inverse problems, remains a long-standing open problem due to the lack of validation data.
An Empirical Comparison of GANs and Normalizing Flows for Density Estimation
Generative adversarial networks (GANs) and normalizing flows are both approaches to density estimation that use deep neural networks to transform samples from an uninformative prior distribution to an approximation of the data distribution.
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective
In this natural geometry-aware way, any metric on the Grassmann manifold can be resided in our model theoreti-cally.
