3 citations · 6 across the 17 of their papers we have counts for
8 papers · 1 filter
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…
Frugal, Flexible, Faithful: Causal Data Simulation via Frengression
Linying Yang, Jens Magelund Tarp, Robin J. Evans +1
Machine learning has revitalized causal inference by combining flexible models and principled estimators, yet robust benchmarking and evaluation remain challenging with real-world…
See-in-Pairs: Reference Image-Guided Comparative Vision-Language Models for Medical Diagnosis
Ruinan Jin, Gexin Huang, Xinwei Shen +3
Medical image diagnosis is challenging because many diseases resemble normal anatomy and exhibit substantial interpatient variability. Clinicians routinely rely on comparative diag…
Towards Better Generalization via Distributional Input Projection Network
Yifan Hao, Yanxin Lu, Hanning Zhang +2
As overparameterized models become increasingly prevalent, training loss alone offers limited insight into generalization performance. While smoothness has been linked to improved…
Causality-Inspired Robustness for Nonlinear Models via Representation Learning
Marin Šola, Peter Bühlmann, Xinwei Shen
Distributional robustness is a central goal of prediction algorithms due to the prevalent distribution shifts in real-world data. The prediction model aims to minimize the worst-ca…
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…