While interest in the application of machine learning to improve healthcare has grown tremendously in recent years, a number of barriers prevent deployment in medical practice.
Single-cell transcriptomics enabled the study of cellular heterogeneity in response to perturbations at the resolution of individual cells.
Thanks to their scalability, two-stage recommenders are used by many of today's largest online platforms, including YouTube, LinkedIn, and Pinterest.
Spurred by tremendous success in pattern matching and prediction tasks, researchers increasingly resort to machine learning to aid original scientific discovery.
Revisiting this limitation from a causal viewpoint we develop a more versatile conceptual framework, causal fairness criteria, and first algorithms to achieve them.
The focus of disentanglement approaches has been on identifying independent factors of variation in data.
Causal treatment effect estimation is a key problem that arises in a variety of real-world settings, from personalized medicine to governmental policy making.
We demonstrate our new sensitivity analysis tools in real-world fairness scenarios to assess the bias arising from confounding.
In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched filtering techniques that are used to detect the gravitational-wave signature of merging black holes.
In this paper, we show that in this selective labels setting, learning a predictor directly only from available labeled data is suboptimal in terms of both fairness and utility.
Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race.
The approach is unsupervised and based on a set of experts that compete for data generated by the mechanisms, driving specialization.
Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning.