1 citations · 1 across the 4 of their papers we have counts for
8 papers
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…
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…
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…
Distributional Principal Autoencoders
Xinwei Shen, Nicolai Meinshausen
Dimension reduction techniques usually lose information in the sense that reconstructed data are not identical to the original data. However, we argue that it is possible to have r…
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…
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…