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Found 7 papers, 1 papers with code
Fair Representation Learning using Interpolation Enabled Disentanglement
In this paper, we propose a novel method to address two key issues: (a) Can we simultaneously learn fair disentangled representations while ensuring the utility of the learned representation for downstream tasks, and (b)Can we provide theoretical insights into when the proposed approach will be both fair and accurate.
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders
Detecting out of distribution (OOD) samples is of paramount importance in all Machine Learning applications.
Out of Distribution (OOD) Detection
Representation Learning
+1
Model Agnostic Multilevel Explanations
The method can also leverage side information, where users can specify points for which they may want the explanations to be similar.
Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
The dearth of prescribing guidelines for physicians is one key driver of the current opioid epidemic in the United States.
Is Ordered Weighted $\ell_1$ Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR
Many state-of-the-art machine learning models such as deep neural networks have recently shown to be vulnerable to adversarial perturbations, especially in classification tasks.
Structure Learning from Time Series with False Discovery Control
Almost all existing Granger causal algorithms condition on a large number of variables (all but two variables) to test for effects between a pair of variables.
Optimized Pre-Processing for Discrimination Prevention
Non-discrimination is a recognized objective in algorithmic decision making.
