Heiko Strathmann

25 papers A* 5A 1Journal 14Unranked 5

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Scaling Instructable Agents Across Many Simulated Worlds. CoRR 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
2023	J	`jnl`	Laser: Latent Set Representations for 3D Generative Modeling. CoRR Pol Moreno, Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Rosália G. Schneider, Björn Winckler, Larisa Markeeva, Théophane Weber, Danilo J. Rezende
2022	A*	`conf`	Score-Based Diffusion meets Annealed Importance Sampling. NeurIPS Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann
2022	J	`jnl`	Score-Based Diffusion meets Annealed Importance Sampling. CoRR Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann
2021	A*	`conf`	NeRF-VAE: A Geometry Aware 3D Scene Generative Model. ICML Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, Danilo Jimenez Rezende
2021	J	`jnl`	NeRF-VAE: A Geometry Aware 3D Scene Generative Model. CoRR Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, Danilo J. Rezende
2021	J	`jnl`	Neural Variational Gradient Descent. CoRR Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann
2021	J	`jnl`	Persistent Message Passing. CoRR Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell, Petar Velickovic
2021	J	`jnl`	Sparse Gaussian Processes on Discrete Domains. IEEE Access Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
2019	A*	`conf`	Learning deep kernels for exponential family densities. ICML Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
2019	—	`conf`	SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
2018	J	`jnl`	Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch
2018	A	`conf`	Efficient and principled score estimation with Nyström kernel exponential families. AISTATS Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
2018	J	`jnl`	Learning deep kernels for exponential family densities. CoRR Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton
2018	J	`jnl`	Scalable Gaussian Processes on Discrete Domains. CoRR Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
2017	J	`jnl`	Efficient and principled score estimation. CoRR Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton
2017	—	`conf`	Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ICLR (Poster) Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
2017	—	`conf`	Kernel Sequential Monte Carlo. ECML/PKDD (1) Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic
2016	A*	`conf`	A Kernel Test of Goodness of Fit. ICML Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
2016	J	`jnl`	Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. CoRR Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton
2015	—	`conf`	Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. NIPS Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton
2015	J	`jnl`	Unbiased Bayes for Big Data: Paths of Partial Posteriors. CoRR Heiko Strathmann, Dino Sejdinovic, Mark A. Girolami
2014	A*	`conf`	Kernel Adaptive Metropolis-Hastings. ICML Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton
2013	J	`jnl`	Kernel Adaptive Metropolis-Hastings. CoRR Dino Sejdinovic, Maria Lomeli Garcia, Heiko Strathmann, Christophe Andrieu, Arthur Gretton
2012	—	`conf`	Optimal kernel choice for large-scale two-sample tests. NIPS Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu