3 citations · 6 across the 17 of their papers we have counts for
17 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…
Distributional Extrapolation for Interactions
Marin Šola, Xinwei Shen, Peter Bühlmann
Predicting combinatorial effects from limited-range observations is a fundamental challenge in many scientific domains, including drug discovery and hyperparameter optimization. We…
Error Analysis of Neural-Network-Based Engression
Juntong Chen, Zijian Guo, Xinwei Shen
Engression (Shen and Meinshausen, 2024) learns a conditional distribution by fitting a generative model under the energy score, a strictly proper scoring rul…
Emputation: Identification-Guided Neural Imputation Framework
Yanjiao Yang, Yikun Zhang, Xinwei Shen +1
We propose Emputation, a deep generative framework for learning imputation models. Emputation targets the extrapolation distribution of missing variables given observed variables,…
Perturbation is All You Need for Extrapolating Language Models
Zetai Cen, Jin Zhu, Xinwei Shen +1
This paper develops a statistical theory of extrapolation for large language models, by reinterpreting them through pre-post-additive noise models. In contrast to the standard auto…
UQ-SHRED: uncertainty quantification of shallow recurrent decoder networks for sparse sensing via engression
Mars Liyao Gao, Yuxuan Bao, Amy S. Rude +2
Reconstructing high-dimensional spatiotemporal fields from sparse sensor measurements is critical in a wide range of scientific applications. The SHallow REcurrent Decoder (SHRED)…
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…
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…
Causality-oriented robustness: exploiting general noise interventions
Xinwei Shen, Peter Bühlmann, Armeen Taeb
Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, suc…
Engression: Extrapolation through the Lens of Distributional Regression
Xinwei Shen, Nicolai Meinshausen
Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates. Popular methods include linear and tree-ensemble based quantile…