Machine Learning Posts - Page 4

Does adding a theorem to any paper increase its chance of acceptance? Which writing style in an email would get the fastest response, regardless of its subject? We introduce algorithms and a python package for measuring the importance of ngram features on an outcome, while controlling for confounds.

Machine learning practitioners are spending less time on model architectures and hardware optimizations and, instead, focusing on training data. We describe three powerful abstractions that practitioners can use to programmatically build and manage their training data.

On learning well-controlled fair representations of data, which make it much more difficult for any downstream machine learning models to discriminate.

An overview of research at SAIL related to new techniques that allow us to look inside the black box of neural networks, to how it is possible to find and remove bias, and to how safety in autonomous systems can be assured.

On uncertainty autoencoders, a new framework for statistical compressed sensing and unsupervised representation learning.

On using weak supervision, or high-level noisy sources of labels, to efficiently label training data.