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Eric P. Xing
Researcher at Carnegie Mellon University
Publications - 725
Citations - 48035
Eric P. Xing is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Inference & Topic model. The author has an hindex of 99, co-authored 711 publications receiving 41467 citations. Previous affiliations of Eric P. Xing include Microsoft & Intel.
Papers
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Patent
Visual representations of structured association mappings
Eric P. Xing,Ross E. Curtis +1 more
TL;DR: In this article, a method performed by one or more processors, comprising: receiving genomic data and trait data representative of a plurality of traits of a single individual or more individuals, determining a structure of one or several of the genomic datasets and the trait data; selecting, in response to the determined structure, a structured association algorithm for execution with the genomic dataset and the traits data; generating, based on execution of the selected, structured association algorithms against the genomic and trait datasets, structured associations indicative of associations among the two datasets, wherein the associations are at least partly identified based on the
Posted Content
Sparse Variable Selection on High Dimensional Heterogeneous Data with Tree Structured Responses
TL;DR: This work introduces a model that can utilize the dependency information from multiple responses to select the active variables from heterogeneous data, and shows that the proposed model outperforms the existing methods.
Journal ArticleDOI
MRCLens: an MRC Dataset Bias Detection Toolkit
TL;DR: This work introduces MRCLens, a toolkit which detects whether bi- 013 ases exist before users train the full model, and provides a categorization of common biases in MRC.
Posted Content
Progressive Generation of Long Text with Pretrained Language Models
TL;DR: This paper proposed a method of generating text in a progressive manner, inspired by generating images from low to high resolution, which first produces domain-specific content keywords and then progressively refines them into complete passages in multiple stages.
Book ChapterDOI
NP-MuScL: unsupervised global prediction of interaction networks from multiple data sources
Kriti Puniyani,Eric P. Xing +1 more
TL;DR: This work proposes NP-MuScL (nonparanormal multi-source learning) to estimate a gene interaction network that is consistent with such multiple data sources, which are expected to reflect the same underlying relationships between the genes.