Optimal kernel choice for large-scale two-sample tests. - Arthur Gretton; Bharath K. Sriperumbudur; Dino Sejdinovic; Heiko Strathmann; Sivaraman Balakrishnan; Massimiliano Pontil; Kenji Fukumizu (NIPS, 2012)

Kernel Adaptive Metropolis-Hastings. - Dino Sejdinovic; Heiko Strathmann; Maria Lomeli Garcia; Christophe Andrieu; Arthur Gretton (ICML, 2014)

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. - Heiko Strathmann; Dino Sejdinovic; Samuel Livingstone; Zoltán Szabó 001; Arthur Gretton (NIPS, 2015)

Unbiased Bayes for Big Data: Paths of Partial Posteriors. - Heiko Strathmann; Dino Sejdinovic; Mark A. Girolami (CoRR, 2015)

A Kernel Test of Goodness of Fit. - Kacper Chwialkowski; Heiko Strathmann; Arthur Gretton (ICML, 2016)

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. - Danica J. Sutherland; Hsiao-Yu Tung; Heiko Strathmann; Soumyajit De; Aaditya Ramdas; Alexander J. Smola; Arthur Gretton (ICLR (Poster), 2017)

Kernel Sequential Monte Carlo. - Ingmar Schuster; Heiko Strathmann; Brooks Paige; Dino Sejdinovic (ECML/PKDD (1), 2017)

Efficient and principled score estimation. - Danica J. Sutherland; Heiko Strathmann; Michael Arbel; Arthur Gretton (CoRR, 2017)

Efficient and principled score estimation with Nyström kernel exponential families. - Danica J. Sutherland; Heiko Strathmann; Michael Arbel; Arthur Gretton (AISTATS, 2018)

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. - Vincent Fortuin; Matthias Hüser; Francesco Locatello; Heiko Strathmann; Gunnar Rätsch (CoRR, 2018)

Scalable Gaussian Processes on Discrete Domains. - Vincent Fortuin; Gideon Dresdner; Heiko Strathmann; Gunnar Rätsch (CoRR, 2018)

SOM-VAE: Interpretable Discrete Representation Learning on Time Series. - Vincent Fortuin; Matthias Hüser; Francesco Locatello; Heiko Strathmann; Gunnar Rätsch (ICLR (Poster), 2019)

Learning deep kernels for exponential family densities. - Wenliang Li; Danica J. Sutherland; Heiko Strathmann; Arthur Gretton (ICML, 2019)

NeRF-VAE: A Geometry Aware 3D Scene Generative Model. - Adam R. Kosiorek; Heiko Strathmann; Daniel Zoran; Pol Moreno; Rosalia Schneider; Sona Mokrá; Danilo Jimenez Rezende (ICML, 2021)

Sparse Gaussian Processes on Discrete Domains. - Vincent Fortuin; Gideon Dresdner; Heiko Strathmann; Gunnar Rätsch (IEEE Access, 2021)

Persistent Message Passing. - Heiko Strathmann; Mohammadamin Barekatain; Charles Blundell; Petar Velickovic (CoRR, 2021)

Neural Variational Gradient Descent. - Lauro Langosco di Langosco; Vincent Fortuin; Heiko Strathmann (CoRR, 2021)

Score-Based Diffusion meets Annealed Importance Sampling. - Arnaud Doucet; Will Grathwohl; Alexander G. de G. Matthews; Heiko Strathmann (NeurIPS, 2022)

Laser: Latent Set Representations for 3D Generative Modeling. - Pol Moreno; Adam R. Kosiorek; Heiko Strathmann; Daniel Zoran; Rosália G. Schneider; Björn Winckler; Larisa Markeeva; Théophane Weber; Danilo J. Rezende (CoRR, 2023)

Scaling Instructable Agents Across Many Simulated Worlds. - SIMA Team; Maria Abi Raad; Arun Ahuja; Catarina Barros; Frederic Besse; Andrew Bolt; Adrian Bolton; Bethanie Brownfield; Gavin Buttimore; Max Cant; Sarah Chakera; Stephanie C. Y. Chan; et al. (CoRR, 2024)