Heiko Strathmann

According to our database, Heiko Strathmann authored at least 20 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

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, Jeff Clune, Adrian Collister, Vikki Copeman, Alex Cullum, Ishita Dasgupta, Dario de Cesare, Julia Di Trapani, Yani Donchev, Emma Dunleavy, Martin Engelcke, Ryan Faulkner, Frankie Garcia, Charles Gbadamosi, Zhitao Gong, Lucy Gonzalez, Kshitij Gupta, Karol Gregor, Arne Olav Hallingstad, Tim Harley, Sam Haves, Felix Hill, Ed Hirst, Drew A. Hudson, Jony Hudson, Steph Hughes-Fitt, Danilo J. Rezende, Mimi Jasarevic, Laura Kampis, Nan Rosemary Ke, Thomas Keck, Junkyung Kim, Oscar Knagg, Kavya Kopparapu, Andrew K. Lampinen, Shane Legg, Alexander Lerchner, Marjorie Limont, Yulan Liu, Maria Loks-Thompson, Joseph Marino, Kathryn Martin Cussons, Loic Matthey, Siobhan Mcloughlin, Piermaria Mendolicchio, Hamza Merzic, Anna Mitenkova, Alexandre Moufarek, Valéria Oliveira, Yanko Gitahy Oliveira, Hannah Openshaw, Renke Pan, Aneesh Pappu, Alex Platonov, Ollie Purkiss, David P. Reichert, John Reid, Pierre Harvey Richemond, Tyson Roberts, Giles Ruscoe, Jaume Sanchez Elias, Tasha Sandars, Daniel P. Sawyer, Tim Scholtes, Guy Simmons, Daniel Slater, Hubert Soyer, Heiko Strathmann, Peter Stys, Allison C. Tam, Denis Teplyashin, Tayfun Terzi, Davide Vercelli, Bojan Vujatovic, Marcus Wainwright, Jane X. Wang, Zhengdong Wang, Daan Wierstra, Duncan Williams, Nathaniel Wong, Sarah York, Nick Young

CoRR, 2024

2023

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

2022

Score-Based Diffusion meets Annealed Importance Sampling.

Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Neural Variational Gradient Descent.

Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann

CoRR, 2021

Persistent Message Passing.

Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell, Petar Velickovic

CoRR, 2021

Sparse Gaussian Processes on Discrete Domains.

Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch

IEEE Access, 2021

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

Proceedings of the 38th International Conference on Machine Learning, 2021

2019

Learning deep kernels for exponential family densities.

Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton

Proceedings of the 36th International Conference on Machine Learning, 2019

SOM-VAE: Interpretable Discrete Representation Learning on Time Series.

Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Scalable Gaussian Processes on Discrete Domains.

Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch

CoRR, 2018

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series.

Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch

CoRR, 2018

Efficient and principled score estimation with Nyström kernel exponential families.

Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Efficient and principled score estimation.

Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton

CoRR, 2017

Kernel Sequential Monte Carlo.

Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

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

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

A Kernel Test of Goodness of Fit.

Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton

Proceedings of the 33nd International Conference on Machine Learning, 2016

2015

Unbiased Bayes for Big Data: Paths of Partial Posteriors.

Heiko Strathmann, Dino Sejdinovic, Mark A. Girolami

CoRR, 2015

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families.

Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Kernel Adaptive Metropolis-Hastings.

Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton

Proceedings of the 31th International Conference on Machine Learning, 2014

2012

Optimal kernel choice for large-scale two-sample tests.

Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012