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ORCIDHeiko Strathmann
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2020 – today
- 2024
[i13]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:
Scaling Instructable Agents Across Many Simulated Worlds. CoRR abs/2404.10179 (2024)- 2023
- [i12]Pol Moreno, Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Rosália G. Schneider, Björn Winckler, Larisa Markeeva, Théophane Weber, Danilo J. Rezende:
Laser: Latent Set Representations for 3D Generative Modeling. CoRR abs/2301.05747 (2023) - 2022
- [c11]Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann:
Score-Based Diffusion meets Annealed Importance Sampling. NeurIPS 2022 - [i11]Arnaud Doucet, Will Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann:
Score-Based Diffusion meets Annealed Importance Sampling. CoRR abs/2208.07698 (2022) - 2021
- [j1]Vincent Fortuin
, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch:
Sparse Gaussian Processes on Discrete Domains. IEEE Access 9: 76750-76758 (2021) - [c10]Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, Danilo Jimenez Rezende:
NeRF-VAE: A Geometry Aware 3D Scene Generative Model. ICML 2021: 5742-5752 - [i10]Heiko Strathmann, Mohammadamin Barekatain, Charles Blundell, Petar Velickovic:
Persistent Message Passing. CoRR abs/2103.01043 (2021) - [i9]Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran, Pol Moreno, Rosalia Schneider, Sona Mokrá, Danilo J. Rezende:
NeRF-VAE: A Geometry Aware 3D Scene Generative Model. CoRR abs/2104.00587 (2021) - [i8]Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann:
Neural Variational Gradient Descent. CoRR abs/2107.10731 (2021)
2010 – 2019
- 2019
- [c9]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
SOM-VAE: Interpretable Discrete Representation Learning on Time Series. ICLR (Poster) 2019 - [c8]Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton:
Learning deep kernels for exponential family densities. ICML 2019: 6737-6746 - 2018
- [c7]Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton:
Efficient and principled score estimation with Nyström kernel exponential families. AISTATS 2018: 652-660 - [i7]Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann, Gunnar Rätsch:
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. CoRR abs/1806.02199 (2018) - [i6]Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch:
Scalable Gaussian Processes on Discrete Domains. CoRR abs/1810.10368 (2018) - [i5]Wenliang Li, Danica J. Sutherland, Heiko Strathmann, Arthur Gretton:
Learning deep kernels for exponential family densities. CoRR abs/1811.08357 (2018) - 2017
- [c6]Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton:
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. ICLR (Poster) 2017 - [c5]Ingmar Schuster, Heiko Strathmann, Brooks Paige, Dino Sejdinovic:
Kernel Sequential Monte Carlo. ECML/PKDD (1) 2017: 390-409 - [i4]Danica J. Sutherland, Heiko Strathmann, Michael Arbel, Arthur Gretton:
Efficient and principled score estimation. CoRR abs/1705.08360 (2017) - 2016
- [c4]Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton:
A Kernel Test of Goodness of Fit. ICML 2016: 2606-2615 - [i3]Danica J. Sutherland, Hsiao-Yu Fish Tung, Heiko Strathmann, Soumyajit De, Aaditya Ramdas, Alexander J. Smola, Arthur Gretton:
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy. CoRR abs/1611.04488 (2016) - 2015
- [c3]Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton:
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. NIPS 2015: 955-963 - [i2]Heiko Strathmann, Dino Sejdinovic, Mark A. Girolami:
Unbiased Bayes for Big Data: Paths of Partial Posteriors. CoRR abs/1501.03326 (2015) - 2014
- [c2]Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia, Christophe Andrieu, Arthur Gretton:
Kernel Adaptive Metropolis-Hastings. ICML 2014: 1665-1673 - 2013
- [i1]Dino Sejdinovic, Maria Lomeli Garcia, Heiko Strathmann, Christophe Andrieu, Arthur Gretton:
Kernel Adaptive Metropolis-Hastings. CoRR abs/1307.5302 (2013) - 2012
- [c1]Arthur Gretton, Bharath K. Sriperumbudur, Dino Sejdinovic, Heiko Strathmann, Sivaraman Balakrishnan, Massimiliano Pontil, Kenji Fukumizu:
Optimal kernel choice for large-scale two-sample tests. NIPS 2012: 1214-1222
Coauthor Index
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