3 citations · 6 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…