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Using the bootstrap to quantify the authority of an empirical ranking

Using the bootstrap to quantify the authority of an empirical ranking

19 November 2009
P. Hall
Hugh Miller
ArXiv (abs)PDFHTML

Papers citing "Using the bootstrap to quantify the authority of an empirical ranking"

7 / 7 papers shown
Prediction-Powered Ranking of Large Language Models
Prediction-Powered Ranking of Large Language Models
Ivi Chatzi
Eleni Straitouri
Suhas Thejaswi
Manuel Gomez Rodriguez
ALM
488
17
0
27 Feb 2024
Lagrangian Inference for Ranking Problems
Lagrangian Inference for Ranking Problems
Yue Liu
Ethan X. Fang
Junwei Lu
BDLFedML
272
28
0
01 Oct 2021
Data-Efficient Methods for Dialogue Systems
Data-Efficient Methods for Dialogue Systems
Igor Shalyminov
318
1
0
05 Dec 2020
Solar: $L_0$ solution path averaging for fast and accurate variable
  selection in high-dimensional data
Solar: L0L_0L0​ solution path averaging for fast and accurate variable selection in high-dimensional data
Ning Xu
Timothy C. G. Fisher
257
0
0
30 Jul 2020
Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation
Hybrid Generative-Retrieval Transformers for Dialogue Domain Adaptation
Igor Shalyminov
Alessandro Sordoni
Adam Atkinson
Hannes Schulz
VLM
202
14
0
03 Mar 2020
The Eighth Dialog System Technology Challenge
The Eighth Dialog System Technology Challenge
Seokhwan Kim
Michel Galley
Chulaka Gunasekara
Sungjin Lee
Adam Atkinson
...
Tim K. Marks
Abhinav Rastogi
Xiaoxue Zang
Srinivas Sunkara
Raghav Gupta
VLM
184
66
0
14 Nov 2019
Modeling the variability of rankings
Modeling the variability of rankings
P. Hall
Hugh Miller
162
36
0
10 Nov 2010
1
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