Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. - Kristopher T. Jensen; Ta-Chu Kao; Marco Tripodi; Guillaume Hennequin (NeurIPS, 2020)

Scalable Bayesian GPFA with automatic relevance determination and discrete noise models. - Kristopher T. Jensen; Ta-Chu Kao; Jasmine Stone; Guillaume Hennequin (NeurIPS, 2021)

Natural continual learning: success is a journey, not (just) a destination. - Ta-Chu Kao; Kristopher T. Jensen; Gido van de Ven; Alberto Bernacchia; Guillaume Hennequin (NeurIPS, 2021)

Natural continual learning: success is a journey, not (just) a destination. - Ta-Chu Kao; Kristopher T. Jensen; Alberto Bernacchia; Guillaume Hennequin (CoRR, 2021)

iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data. - Marine Schimel; Ta-Chu Kao; Kristopher T. Jensen; Guillaume Hennequin (ICLR, 2022)

Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. - Martin Bjerke; Lukas Schott; Kristopher T. Jensen; Claudia Battistin; David A. Klindt; Benjamin Adric Dunn (ICLR, 2023)

An introduction to reinforcement learning for neuroscience. - Kristopher T. Jensen (CoRR, 2023)