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1606.07081
Cited By
Finite Sample Prediction and Recovery Bounds for Ordinal Embedding
22 June 2016
Lalit P. Jain
Kevin G. Jamieson
Robert D. Nowak
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Papers citing
"Finite Sample Prediction and Recovery Bounds for Ordinal Embedding"
13 / 13 papers shown
Title
Dimensions underlying the representational alignment of deep neural networks with humans
F. Mahner
Lukas Muttenthaler
Umut Güçlü
M. Hebart
50
5
0
28 Jan 2025
Stability of Sequential Lateration and of Stress Minimization in the Presence of Noise
E. Arias-Castro
Phong Alain Chau
19
1
0
17 Oct 2023
Tight and fast generalization error bound of graph embedding in metric space
Atsushi Suzuki
Atsushi Nitanda
Taiji Suzuki
Jing Wang
Feng Tian
Kenji Yamanishi
23
0
0
13 May 2023
One for All: Simultaneous Metric and Preference Learning over Multiple Users
Gregory H. Canal
Blake Mason
Ramya Korlakai Vinayak
R. Nowak
FedML
17
11
0
07 Jul 2022
Generalization Error Bound for Hyperbolic Ordinal Embedding
Atsushi Suzuki
Atsushi Nitanda
Jing Wang
Linchuan Xu
M. Cavazza
Kenji Yamanishi
22
10
0
21 May 2021
Simultaneous Preference and Metric Learning from Paired Comparisons
Austin Xu
Mark A. Davenport
19
18
0
04 Sep 2020
Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee
Mikhail Yurochkin
Moulinath Banerjee
Yuekai Sun
FaML
26
96
0
19 Jun 2020
Insights into Ordinal Embedding Algorithms: A Systematic Evaluation
L. C. Vankadara
Siavash Haghiri
Michael Lohaus
Faiz Ul Wahab
U. V. Luxburg
18
8
0
03 Dec 2019
Fast Stochastic Ordinal Embedding with Variance Reduction and Adaptive Step Size
Ke Ma
Jinshan Zeng
Qianqian Xu
Xiaochun Cao
Wei Liu
Yuan Yao
33
3
0
01 Dec 2019
Uncertainty Estimates for Ordinal Embeddings
Michael Lohaus
Philipp Hennig
U. V. Luxburg
39
6
0
27 Jun 2019
Foundations of Comparison-Based Hierarchical Clustering
D. Ghoshdastidar
Michaël Perrot
U. V. Luxburg
19
26
0
02 Nov 2018
On the Estimation of Latent Distances Using Graph Distances
E. Arias-Castro
Antoine Channarond
Bruno Pelletier
Nicolas Verzélen
20
15
0
27 Apr 2018
Lens depth function and k-relative neighborhood graph: versatile tools for ordinal data analysis
Matthäus Kleindessner
U. V. Luxburg
23
33
0
23 Feb 2016
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