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T. Xie

4 papers hereh-index 340 citations7 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG3
  • cs.IT1
same name
  • T. Xie — 28 papers, h 14
  • T. Xie — 15 papers, h 16
  • T. Xie — 8 papers, h 11
  • T. Xie — 5 papers, h 7
  • T. Xie — 4 papers, h 3
  • T. Xie — 4 papers, h 3

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152017
most citedSemi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination

4 citations · 6 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2017

Semiblind subgraph reconstruction in Gaussian graphical models

Tianpei Xie, Sijia Liu, Alfred O. Hero

Consider a social network where only a few nodes (agents) have meaningful interactions in the sense that the conditional dependency graph over node attribute variables (behaviors)…

cs.LG2016

Robust training on approximated minimal-entropy set

Tianpei Xie, Nasser. M. Narabadi, Alfred O. Hero

In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be presen…

cs.IT2015★ 4 cited

Semi-supervised Multi-sensor Classification via Consensus-based Multi-View Maximum Entropy Discrimination

Tianpei Xie, Nasser M. Nasrabadi, Alfred O. Hero

In this paper, we consider multi-sensor classification when there is a large number of unlabeled samples. The problem is formulated under the multi-view learning framework and a Co…

cs.LG2015★ 2 cited

Learning to classify with possible sensor failures

Tianpei Xie, Nasser M. Nasrabadi, Alfred O. Hero

In this paper, we propose a general framework to learn a robust large-margin binary classifier when corrupt measurements, called anomalies, caused by sensor failure might be presen…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.