2.5k citations · 4k across the 54 of their papers we have counts for
63 papers
GCMagicc v1: a fast generative emulator for multivariate climate-impact ensembles
Nicolai Meinshausen, Malte Meinshausen, Jared Lewis +6
Projecting the impacts of climate change requires large ensembles of climate variables that match historical observations, align with the warming ranges assessed by the IPCC, and c…
Anti-causal domain generalization: Leveraging unlabeled data
Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller +3
The problem of domain generalization concerns learning predictive models that are robust to distribution shifts when deployed in new, previously unseen environments. Existing metho…
EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules
Maybritt Schillinger, Maxim Samarin, Xinwei Shen +2
The practical use of future climate projections from global circulation models (GCMs) is often limited by their coarse spatial resolution, requiring downscaling to generate high-re…
Extrapolation Guarantees for Perturbation Modeling Under the Additive Latent Shift Assumption
Julius von Kügelgen, Jakob Ketterer, Michael Vollenweider +4
We consider the problem of modeling the effects of perturbations like gene knockouts on measurements such as single-cell RNA counts. Given data for some perturbations, we aim to pr…
Reverse Markov Learning: Multi-Step Generative Models for Complex Distributions
Xinwei Shen, Nicolai Meinshausen, Tong Zhang
Learning complex distributions is a fundamental challenge in contemporary applications. Shen and Meinshausen (2024) introduced engression, a generative approach based on scoring ru…
Distributional Instrumental Variable Method
Anastasiia Holovchak, Sorawit Saengkyongam, Nicolai Meinshausen +1
The instrumental variable (IV) approach is commonly used to infer causal effects in the presence of unmeasured confounding. Existing methods typically aim to estimate the mean caus…