Multi-path Neural Networks for On-device Multi-domain Visual Classification. - Qifei Wang; Junjie Ke; Joshua Greaves; Grace Chu; Gabriel Bender; Luciano Sbaiz; Alec Go; Andrew G. Howard; Ming-Hsuan Yang 001; Jeff Gilbert; Peyman Milanfar; Feng Yang 008 (WACV, 2021)

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces. - Charline Le Lan; Joshua Greaves; Jesse Farebrother; Mark Rowland 001; Fabian Pedregosa; Rishabh Agarwal; Marc G. Bellemare (AISTATS, 2023)

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks. - Jesse Farebrother; Joshua Greaves; Rishabh Agarwal; Charline Le Lan; Ross Goroshin; Pablo Samuel Castro; Marc G. Bellemare (ICLR, 2023)

Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy. - Max Schwarzer; Jesse Farebrother; Joshua Greaves; Ekin Dogus Cubuk; Rishabh Agarwal; Aaron C. Courville; Marc G. Bellemare; Sergei V. Kalinin; Igor Mordatch; Pablo Samuel Castro; Kevin M. Roccapriore (CoRR, 2023)

Tapered Off-Policy REINFORCE: Stable and efficient reinforcement learning for LLMs. - Nicolas Le Roux; Marc G. Bellemare; Jonathan Lebensold; Arnaud Bergeron; Joshua Greaves; Alexandre Fréchette; Carolyne Pelletier; Eric Thibodeau-Laufer; Sándor Tóth; Sam Work (CoRR, 2025)

Beyond Monoliths: Expert Orchestration for More Capable, Democratic, and Safe Large Language Models. - Philip Quirke; Narmeen Oozeer; Chaithanya Bandi; Amir Abdullah; Jason Hoelscher-Obermaier; Jeff M. Phillips; Joshua Greaves; Clement Neo; Fazl Barez; Shriyash Upadhyay (CoRR, 2025)