no code implementations • 11 Dec 2023 • Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy McGibbon, Christopher Bretherton, Stephan Mandt
In climate science and meteorology, high-resolution local precipitation (rain and snowfall) predictions are limited by the computational costs of simulation-based methods.
1 code implementation • 3 Oct 2023 • Oliver Watt-Meyer, Gideon Dresdner, Jeremy McGibbon, Spencer K. Clark, Brian Henn, James Duncan, Noah D. Brenowitz, Karthik Kashinath, Michael S. Pritchard, Boris Bonev, Matthew E. Peters, Christopher S. Bretherton
Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency.
2 code implementations • 1 Jul 2022 • Gideon Dresdner, Dmitrii Kochkov, Peter Norgaard, Leonardo Zepeda-Núñez, Jamie A. Smith, Michael P. Brenner, Stephan Hoyer
We build upon Fourier-based spectral methods, which are known to be more efficient than other numerical schemes for simulating PDEs with smooth and periodic solutions.
1 code implementation • 26 Feb 2022 • Gideon Dresdner, Maria-Luiza Vladarean, Gunnar Rätsch, Francesco Locatello, Volkan Cevher, Alp Yurtsever
We propose a stochastic conditional gradient method (CGM) for minimizing convex finite-sum objectives formed as a sum of smooth and non-smooth terms.
1 code implementation • 9 Jun 2021 • Hugo Yèche, Gideon Dresdner, Francesco Locatello, Matthias Hüser, Gunnar Rätsch
Intensive care units (ICU) are increasingly looking towards machine learning for methods to provide online monitoring of critically ill patients.
no code implementations • 19 May 2021 • Gideon Dresdner, Saurav Shekhar, Fabian Pedregosa, Francesco Locatello, Gunnar Rätsch
Variational Inference makes a trade-off between the capacity of the variational family and the tractability of finding an approximate posterior distribution.
1 code implementation • ICML 2020 • Geoffrey Négiar, Gideon Dresdner, Alicia Tsai, Laurent El Ghaoui, Francesco Locatello, Robert M. Freund, Fabian Pedregosa
We propose a novel Stochastic Frank-Wolfe (a. k. a.
no code implementations • 24 Oct 2018 • Vincent Fortuin, Gideon Dresdner, Heiko Strathmann, Gunnar Rätsch
We explore different techniques for selecting inducing points on discrete domains, including greedy selection, determinantal point processes, and simulated annealing.
1 code implementation • NeurIPS 2018 • Francesco Locatello, Gideon Dresdner, Rajiv Khanna, Isabel Valera, Gunnar Rätsch
Finally, we present a stopping criterion drawn from the duality gap in the classic FW analyses and exhaustive experiments to illustrate the usefulness of our theoretical and algorithmic contributions.