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J. Carbonell

47 papers hereh-index 7740.5k citations500 works total

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

author position
  • middle author20
  • last author26

Across the 46 of 47 papers where every author was matched, so the position is known.

fields
  • cs.CL21
  • cs.LG13
  • cs.IR5
  • stat.ML5
  • cs.CY2
  • eess.SY1
same name
  • J. Carbonell — 99 papers, h 28
  • J. Carbonell — 11 papers, h 6
  • J. Carbonell — 7 papers, h 3
  • J. Carbonell — 5 papers, h 17
  • J. Carbonell — 2 papers
  • J. Carbonell — 2 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
20102020
most citedXLNet: Generalized Autoregressive Pretraining for Language Understanding

1.8k citations · 2.4k across the 40 of their papers we have counts for

collaborators
Showing 2015Show all

4 papers · 1 filter

eess.SY2015★ 13 cited

Efficient Structured Matrix Rank Minimization

Adams Wei Yu, Wanli Ma, Yaoliang Yu +2

We study the problem of finding structured low-rank matrices using nuclear norm regularization where the structure is encoded by a linear map. In contrast to most known approaches…

cs.IR2015★ 9 cited

Privacy-Preserving Multi-Document Summarization

Luís Marujo, José Portêlo, Wang Ling +6

State-of-the-art extractive multi-document summarization systems are usually designed without any concern about privacy issues, meaning that all documents are open to third parties…

cs.IR2015

Extending a Single-Document Summarizer to Multi-Document: a Hierarchical Approach

Luís Marujo, Ricardo Ribeiro, David Martins de Matos +3

The increasing amount of online content motivated the development of multi-document summarization methods. In this work, we explore straightforward approaches to extend single-docu…

cs.LG2015★ 1 cited

Bounds on the Minimax Rate for Estimating a Prior over a VC Class from Independent Learning Tasks

Liu Yang, Steve Hanneke, Jaime Carbonell

We study the optimal rates of convergence for estimating a prior distribution over a VC class from a sequence of independent data sets respectively labeled by independent target fu…

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