These processes find wide application in the natural sciences and machine learning, but their inference is known to be far from trivial.

Unlike the classic utility maximizers who maximize their quasi-linear utility given by the difference between value and payment, value maximizers maximize the total value subject to a return-on-spend (RoS) constraint [Balseiro et al., 2021b].

To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models.

The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroen-cephalography (EEG) and electromyography (EMG) recordings.

When deploying a machine learning model in production, it is common to encounter changes in the data distribution, such as shifts in covariates [Shimodaira, 2000], labels [Saerens et al., 2002,

Novel view synthesis of dynamic scenes is becoming important in various applications, including augmented and virtual reality.