2 citations · 3 across the 6 of their papers we have counts for
6 papers
Assessing the Robustness of Climate Foundation Models under No-Analog Distribution Shifts
Maria Conchita Agana Navarro, Geng Li, Theo Wolf +1
The accelerating pace of climate change introduces profound non-stationarities that challenge the ability of Machine Learning based climate emulators to generalize beyond their tra…
DiscoGen: Procedural Generation of Algorithm Discovery Tasks in Machine Learning
Alexander D. Goldie, Zilin Wang, Adrian Hayler +17
Automating the development of machine learning algorithms has the potential to unlock new breakthroughs. However, our ability to improve and evaluate algorithm discovery systems ha…
Evolution Strategies at the Hyperscale
Bidipta Sarkar, Mattie Fellows, Juan Agustin Duque +17
Evolution Strategies (ES) is a class of powerful black-box optimisation methods that are highly parallelisable and can handle non-differentiable and noisy objectives. However, naïv…
Do machine learning climate models work in changing climate dynamics?
Maria Conchita Agana Navarro, Geng Li, Theo Wolf +1
Climate change is accelerating the frequency and severity of unprecedented events, deviating from established patterns. Predicting these out-of-distribution (OOD) events is critica…
Can Reinforcement Learning support policy makers? A preliminary study with Integrated Assessment Models
Theodore Wolf, Nantas Nardelli, John Shawe-Taylor +1
Governments around the world aspire to ground decision-making on evidence. Many of the foundations of policy making - e.g. sensing patterns that relate to societal needs, developin…
Climate Change Policy Exploration using Reinforcement Learning
Theodore Wolf
Climate Change is an incredibly complicated problem that humanity faces. When many variables interact with each other, it can be difficult for humans to grasp the causes and effect…