Prospective Learning: Back to the Future. - Joshua T. Vogelstein; Timothy D. Verstynen; Konrad P. Kording; Leyla Isik; John W. Krakauer; Ralph Etienne-Cummings; Elizabeth L. Ogburn; Carey E. Priebe; Randal C. Burns; Kwame S. Kutten; James J. Knierim; James B. Potash; et al. (CoRR, 2022)

Prospective Learning: Principled Extrapolation to the Future. - Ashwin De Silva; Rahul Ramesh; Lyle H. Ungar; Marshall G. Hussain Shuler; Noah J. Cowan; Michael L. Platt; Chen Li; Leyla Isik; Seung-Eon Roh; Adam S. Charles; Archana Venkataraman; Brian Caffo; et al. (CoLLAs, 2023)

Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics. - Noga Mudrik; Yenho Chen; Eva Yezerets; Christopher J. Rozell; Adam S. Charles (J. Mach. Learn. Res., 2024)

LINOCS: Lookahead Inference of Networked Operators for Continuous Stability. - Noga Mudrik; Eva Yezerets; Yenho Chen; Christopher John Rozell; Adam Shabti Charles (Trans. Mach. Learn. Res., 2024)