Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning. - Thomas Baldwin-McDonald; Mauricio A. Álvarez (CoRR, 2023)

GP-PCS: One-Shot Feature-Preserving Point Cloud Simplification with Gaussian Processes on Riemannian Manifolds. - Stuti Pathak 001; Thomas Baldwin-McDonald; Seppe Sels; Rudi Penne (ICPR (18), 2024)

Adaptive RKHS Fourier Features for Compositional Gaussian Process Models. - Xinxing Shi; Thomas Baldwin-McDonald; Mauricio A. Álvarez (AISTATS, 2025)

Impatient Bandits: Optimizing for the Long-Term Without Delay. - Kelly W. Zhang; Thomas Baldwin-McDonald; Kamil Ciosek; Lucas Maystre; Daniel Russo 001 (CoRR, 2025)

Towards deployment-centric multimodal AI beyond vision and language. - Xianyuan Liu; Jiayang Zhang; Shuo Zhou; Thijs L. van der Plas; Avish Vijayaraghavan; Anastasiia Grishina; Mengdie Zhuang; Daniel Schofield; Christopher Tomlinson; Yuhan Wang; Ruizhe Li; Louisa van Zeeland; et al. (CoRR, 2025)

Deep latent force models: ODE-based process convolutions for Bayesian deep learning. - Thomas Baldwin-McDonald; Xinxing Shi; Mingxin Shen; Mauricio A. Álvarez (Mach. Learn., 2025)