Latent Gaussian Process Regression. - Erik Bodin; Neill D. F. Campbell; Carl Henrik Ek (CoRR, 2017)

Nonparametric Inference for Auto-Encoding Variational Bayes. - Erik Bodin; Iman Malik; Carl Henrik Ek; Neill D. F. Campbell (CoRR, 2017)

Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation. - Alessandro Di Martino; Erik Bodin; Carl Henrik Ek; Neill D. F. Campbell (ACCV (4), 2018)

Modulated Bayesian Optimization using Latent Gaussian Process Models. - Erik Bodin; Markus Kaiser 001; Ieva Kazlauskaite; Neill D. F. Campbell; Carl Henrik Ek (CoRR, 2019)

Modulating Surrogates for Bayesian Optimization. - Erik Bodin; Markus Kaiser 001; Ieva Kazlauskaite; Zhenwen Dai; Neill W. Campbell; Carl Henrik Ek (ICML, 2020)

Compositional uncertainty in deep Gaussian processes. - Ivan Ustyuzhaninov; Ieva Kazlauskaite; Markus Kaiser 001; Erik Bodin; Neill D. F. Campbell; Carl Henrik Ek (UAI, 2020)

Black-box density function estimation using recursive partitioning. - Erik Bodin; Zhenwen Dai; Neill D. F. Campbell; Carl Henrik Ek (CoRR, 2020)

Black-box density function estimation using recursive partitioning. - Erik Bodin; Zhenwen Dai; Neill W. Campbell; Carl Henrik Ek (ICML, 2021)

Making Differentiable Architecture Search less local. - Erik Bodin; Federico Tomasi; Zhenwen Dai (CoRR, 2021)

Linear combinations of latents in diffusion models: interpolation and beyond. - Erik Bodin; Henry Moss; Carl Henrik Ek (CoRR, 2024)

Linear combinations of latents in generative models: subspaces and beyond. - Erik Bodin; Alexandru I. Stere; Dragos D. Margineantu; Carl Henrik Ek; Henry Moss (ICLR, 2025)