However, GCP-CROWN currently relies on generic cutting planes ("cuts") generated from external

Leveraging this result, our work provides the first provably efficient implementation of PseudoPML.

Most existing work in this area relies on non-probabilistic approaches or on complex Reinforcement Learning (RL)-based hard structures.

With feedbackfromthesevolunteers,wedevelopedalightweight,smallandunobtrusive prototype equipped with a tiny stereo camera and an embedded system on a single board computer to capture the samples from 10 different locations. We describe instrument development, datacollection, preprocessing, annotation, and experiments conducted.

P2SROisabletoparallelize PSROwith convergence guarantees bymaintaining ahierarchical pipeline ofreinforcement learning workers, each training against the policies generated by lower levels in the hierarchy.

InMachine Learning Challenges Workshop, pages 177-190.

Despite the widespread success of Transformers across various domains, their optimization guarantees in large-scale model settings are not well-understood.