To address these questions, we propose analyzing a model's learning

Topic models have been evolving rapidly over the years, from conventional to recent neural models.

Most of the literature in fair machine learning focuses on defining and achieving fairness criteria in the context of prediction, while not explicitly focusing on how these predictions may be used later on in the pipeline.

While tabular classification has traditionally relied on from-scratch training, a recent breakthrough called prior-data fitted networks (PFNs) challenges this approach. Similar to large language models, PFNs make use of pretraining and in-context learning to achieve strong performance on new tasks in a single forward pass.

In this paper, we introduce a new type of model backdoor: the privacy backdoor attack .

To address issues of group-level fairness in machine learning, it is natural to adjust model parameters based on specific fairness objectives over a sensitive-attributed validation set.

The latest film in the franchise seems to have not learned any lessons from sci-fi movies past--or from current reality. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Ares, named after the Greek god of war, was built to be an AI super-soldier. Then he found out about, started listening to Depeche Mode, and realized the tech bro who made him might be a hack.