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Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset.
Active learning for graph neural networks via node feature propagation
Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, and Artur Dubrawski · 1910
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The maximal covering location problem
Richard Church and Charles ReVelle · 1974
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Clustering to minimize the maximum intercluster distance
Teofilo F. Gonzalez · 1985
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Algorithms for Clustering Data
A.K. Jain and R.C. Dubes · 1988
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Novel approaches to the discrimination problem
Patrice Marcotte and Gilles Savard · 1992
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Active learning with statistical models
David A Cohn, Zoubin Ghahramani, and Michael I Jordan · 1996
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Estimation and prediction for stochastic blockmodels for graphs with latent block structure
Tom AB Snijders and Krzysztof Nowicki · 1997
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Learning by transduction
A. Gammerman, V. Vovk, and V. Vapnik · 1998
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Analysis of the greedy approach in problems of maximum k-coverage
Dorit S Hochbaum and Anu Pathria · 1998
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An optimal representative set selection method
J.G. Lee and C.G. Chung · 2000
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On spectral clustering: Analysis and an algorithm
Andrew Ng, Michael Jordan, and Yair Weiss · 2001
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A local search approximation algorithm for k-means clustering
Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and Angela Y. Wu · 2002
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Minimum redundancy feature selection from microarray gene expression data
Chris H. Q. Ding and Hanchuan Peng · 2003
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Finding representative set from massive data
Feng Pan, Wei Wang, Anthony K. H. Tung, and Jiong Yang · 2005
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Survey of clustering algorithms
Rui Xu and Donald Wunsch · 2005
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Batch mode active learning and its application to medical image classification
Steven CH Hoi, Rong Jin, Jianke Zhu, and Michael R Lyu · 2006
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Data Clustering: Theory, Algorithms, and Applications
Guojun Gan, Chaoqun Ma, and Jianhong Wu · 2007
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Discriminative batch mode active learning
Yuhong Guo and Dale Schuurmans · 2007
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Conditional mutual information based feature selection for classification task
Jana Novovicová, Petr Somol, Michal Haindl, and Pavel Pudil · 2007
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Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton · 2008
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction
T. Hastie, R. Tibshirani, and J.H. Friedman · 2009
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Finding Groups in Data: An Introduction to Cluster Analysis
L. Kaufman and P.J. Rousseeuw · 2009
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Active learning literature survey
Burr Settles · 2009
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Coresets and Their Applications
Dan Feldman · 2010
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Lower bounds based on the exponential time hypothesis
Daniel Lokshtanov, Dániel Marx, Saket Saurabh, et al · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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The exponential time hypothesis and the parameterized clique problem
Yijia Chen, Kord Eickmeyer, and Jörg Flum · 2012
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Active Learning and Submodular Functions
Andrew Guillory · 2012
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Diversity maximization under matroid constraints
Zeinab Abbassi, Vahab S. Mirrokni, and Mayur Thakur · 2013
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Near-optimal batch mode active learning and adaptive submodular optimization
Yuxin Chen and Andreas Krause · 2013
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Complexity of sat problems, clone theory and the exponential time hypothesis
Peter Jonsson, Victor Lagerkvist, Gustav Nordh, and Bruno Zanuttini · 2013
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Approximation Algorithms
V.V. Vazirani · 2013
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Approximating the best nash equilibrium in no (log n)-time breaks the exponential time hypothesis
Mark Braverman, Young Kun Ko, and Omri Weinstein · 2014
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Adaptive batch mode active learning
Shayok Chakraborty, Vineeth Balasubramanian, and Sethuraman Panchanathan · 2014
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Composable core-sets for diversity and coverage maximization
Piotr Indyk, Sepideh Mahabadi, Mohammad Mahdian, and Vahab S. Mirrokni · 2014
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Efficient monte carlo and greedy heuristic for the inference of stochastic block models
Tiago P Peixoto · 2014
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Submodular subset selection for large-scale speech training data
Kai Wei, Yuzong Liu, Katrin Kirchhoff, Chris Bartels, and Jeff Bilmes · 2014
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The hardness of approximation of euclidean k-means
Pranjal Awasthi, Moses Charikar, Ravishankar Krishnaswamy, and Ali Kemal Sinop · 2015
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Summarization of multi-document topic hierarchies using submodular mixtures
Ramakrishna Bairi, Rishabh Iyer, Ganesh Ramakrishnan, and Jeff Bilmes · 2015
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Recent advances and emerging challenges of feature selection in the context of big data
Verónica Bolón-Canedo, Noelia Sánchez-Maroño, and Amparo Alonso-Betanzos · 2015
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Grarep: Learning graph representations with global structural information
Shaosheng Cao, Wei Lu, and Qiongkai Xu · 2015
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Lower bounds based on the exponential-time hypothesis
Marek Cygan, Fedor V Fomin, Łukasz Kowalik, Daniel Lokshtanov, Dániel Marx, Marcin Pilipczuk, Michał Pilipczuk, and Saket Saurabh · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Svitchboard ii and fisver i: High-quality limited-complexity corpora of conversational english speech
Yuzong Liu, Rishabh Iyer, Katrin Kirchhoff, and Jeff Bilmes · 2015
Cited alongside, same era.
Submodularity in data subset selection and active learning
Kai Wei, Rishabh Iyer, and Jeff Bilmes · 2015
Cited alongside, same era.
A comprehensive survey of clustering algorithms
Structural deep clustering network
Deyu Bo, Xiao Wang, Chuan Shi, Meiqi Zhu, Emiao Lu, and Peng Cui · 2020
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Machine learning on graphs: A model and comprehensive taxonomy
Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, and Kevin Murphy · 2020
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Core-sets: Updated survey
Dan Feldman · 2020
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Sign: Scalable inception graph neural networks
Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti · 2020
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Jraph: A library for graph neural networks in jax., 2020
Jonathan Godwin*, Thomas Keck*, Peter Battaglia, Victor Bapst, Thomas Kipf, Yujia Li, Kimberly Stachenfeld, Petar Veličković, and Alvaro Sanchez-Gonzalez · 2020
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Dongkuan Xu and Yingjie Tian · 2015
Cited alongside, same era.
Linear relaxations for finding diverse elements in metric spaces
Aditya Bhaskara, Mehrdad Ghadiri, Vahab S. Mirrokni, and Ola Svensson · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Improved and simplified inapproximability for k-means
Euiwoong Lee, Melanie Schmidt, and John Wright · 2016
Cited alongside, same era.
Eth hardness for densest-k-subgraph with perfect completeness
Mark Braverman, Young Kun Ko, Aviad Rubinstein, and Omri Weinstein · 2017
Cited alongside, same era.
Active learning for graph embedding
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang · 2017
Cited alongside, same era.
Flax: A neural network library and ecosystem for JAX, 2020
Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
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Contextual reserve price optimization in auctions via mixed-integer programming
Joey Huchette, Haihao Lu, Hossein Esfandiari, and Vahab Mirrokni · 2020
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Coresets for data-efficient training of machine learning models
Baharan Mirzasoleiman, Jeff Bilmes, and Jure Leskovec · 2020
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Self-representation based unsupervised exemplar selection in a union of subspaces
Chong You, Chi Li, Daniel Robinson, and Rene Vidal · 2020
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Structpool: Structured graph pooling via conditional random fields
Hao Yuan and Shuiwang Ji · 2020
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Structure-feature based graph self-adaptive pooling
Liang Zhang, Xudong Wang, Hongsheng Li, Guangming Zhu, Peiyi Shen, Ping Li, Xiaoyuan Lu, Syed Afaq Ali Shah, and Mohammed Bennamoun · 2020
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Fair near neighbor search via sampling
Martin Aumüller, Sariel Har-Peled, Sepideh Mahabadi, Rasmus Pagh, and Francesco Silvestri · 2021
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Graph convolution for semi-supervised classification: Improved linear separability and out-of-distribution generalization
Aseem Baranwal, Kimon Fountoulakis, and Aukosh Jagannath · 2021
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Feature cross search via submodular optimization
Lin Chen, Hossein Esfandiari, Gang Fu, Vahab S Mirrokni, and Qian Yu · 2021
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Adaptive universal generalized pagerank graph neural network, 2021
Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic · 2021
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Batch active learning at scale
Gui Citovsky, Giulia DeSalvo, Claudio Gentile, Lazaros Karydas, Anand Rajagopalan, Afshin Rostamizadeh, and Sanjiv Kumar · 2021
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Vq-gnn: A universal framework to scale up graph neural networks using vector quantization
Mucong Ding, Kezhi Kong, Jingling Li, Chen Zhu, John Dickerson, Furong Huang, and Tom Goldstein · 2021
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Training data subset selection for regression with controlled generalization error
S Durga, Rishabh Iyer, Ganesh Ramakrishnan, and Abir De · 2021
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Adaptivity in adaptive submodularity
Hossein Esfandiari, Amin Karbasi, and Vahab Mirrokni · 2021
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Slaps: Self-supervision improves structure learning for graph neural networks
Bahare Fatemi, Layla El Asri, and Seyed Mehran Kazemi · 2021
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ipool–information-based pooling in hierarchical graph neural networks
Xing Gao, Wenrui Dai, Chenglin Li, Hongkai Xiong, and Pascal Frossard · 2021
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Graph autoencoder for graph compression and representation learning
Yunhao Ge, Yunkui Pang, Linwei Li, and Laurent Itti · 2021
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Faircrowd: Fair human face dataset sampling via batch-level crowdsourcing bias inference
Ziyi Kou, Yang Zhang, Lanyu Shang, and Dong Wang · 2021
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Machine learning robustness, fairness, and their convergence
Jae-Gil Lee, Yuji Roh, Hwanjun Song, and Steven Euijong Whang · 2021
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Hierarchical adaptive pooling by capturing high-order dependency for graph representation learning
Ning Liu, Songlei Jian, Dongsheng Li, Yiming Zhang, Zhiquan Lai, and Hongzuo Xu · 2021
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Graph pooling via coarsened graph infomax
Yunsheng Pang, Yunxiang Zhao, and Dongsheng Li · 2021
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Deep learning on a data diet: Finding important examples early in training
Mansheej Paul, Surya Ganguli, and Gintare Karolina Dziugaite · 2021
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Attention-driven graph clustering network
Zhihao Peng, Hui Liu, Yuheng Jia, and Junhui Hou · 2021
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Fairbatch: Batch selection for model fairness
Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh · 2021
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Adaptive sampling for minimax fair classification
Shubhanshu Shekhar, Greg Fields, Mohammad Ghavamzadeh, and Tara Javidi · 2021
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Grain: Improving data efficiency of graph neural networks via diversified influence maximization
Wentao Zhang, Zhi Yang, Yexin Wang, Yu Shen, Yang Li, Liang Wang, and Bin Cui · 2021
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A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects
Absalom E. Ezugwu, Abiodun M. Ikotun, Olaide O. Oyelade, Laith Abualigah, Jeffery O. Agushaka, Christopher I. Eke, and Andronicus A. Akinyelu · 2022
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Efficient graph convolution for joint node representation learning and clustering
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Grace: A general graph convolution framework for attributed graph clustering
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Graphworld: Fake graphs bring real insights for gnns
John Palowitch, Anton Tsitsulin, Brandon Mayer, and Bryan Perozzi · 2022
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Effects of graph convolutions in multi-layer networks
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Ugsl: A unified framework for benchmarking graph structure learning
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