Fetching the paper…
Reading the bibliography…
The goal in extreme multi-label classification is to learn a classifier which can assign a small subset of relevant labels to an instance from an extremely large set of target labels.
Spectral graph theory
F. R. Chung · 1997
Earlier work this paper cites.
A douglas–rachford splitting approach to nonsmooth convex variational signal recovery
P. L. Combettes and J.-C. Pesquet · 2007
Earlier work this paper cites.
Liblinear: A library for large linear classification
R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin · 2008
Earlier work this paper cites.
Efficient pairwise multilabel classification for large-scale problems in the legal domain
E. L. Mencia and J. Fürnkranz · 2008
Earlier work this paper cites.
Redundancy, diversity and interdependent document relevance
F. Radlinski, P. N. Bennett, B. Carterette, and T. Joachims · 2009
Earlier work this paper cites.
Mining multi-label data
G. Tsoumakas, I. Katakis, and I. Vlahavas · 2009
Earlier work this paper cites.
Robustness and regularization of support vector machines
H. Xu, C. Caramanis, and S. Mannor · 2009
Earlier work this paper cites.
What does classifying more than 10,000 image categories tell us?
J. Deng, A. C. Berg, K. Li, and L. Fei-Fei · 2010
Earlier work this paper cites.
Robust regression and lasso
H. Xu, C. Caramanis, and S. Mannor · 2010
Earlier work this paper cites.
Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages
R. Agrawal, A. Gupta, Y. Prabhu, and M. Varma · 2013
Earlier work this paper cites.
On flat versus hierarchical classification in large-scale taxonomies
R. Babbar, I. Partalas, E. Gaussier, and M.-R. Amini · 2013
Earlier work this paper cites.
Hidden factors and hidden topics: understanding rating dimensions with review text
J. McAuley and J. Leskovec · 2013
Earlier work this paper cites.
Tutorial on application-oriented evaluation of recommendation systems
G. Shani and A. Gunawardana · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
Earlier work this paper cites.
On power law distributions in large-scale taxonomies
R. Babbar, C. Metzig, I. Partalas, E. Gaussier, and M.-R. Amini · 2014
Earlier work this paper cites.
Re-ranking approach to classification in large-scale power-law distributed category systems
R. Babbar, I. Partalas, E. Gaussier, and M.-R. Amini · 2014
Cited alongside, same era.
Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
Cited alongside, same era.
Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning
Y. Prabhu and M. Varma · 2014
Cited alongside, same era.
Large-scale multi-label learning with missing labels
H.-F. Yu, P. Jain, P. Kar, and I. Dhillon · 2014
Cited alongside, same era.
Sparse local embeddings for extreme multi-label classification
K. Bhatia, H. Jain, P. Kar, M. Varma, and P. Jain · 2015
Cited alongside, same era.
Robust extreme multi-label learning
C. Xu, D. Tao, and C. Xu · 2016
Later among the works it cites.
Pd-sparse : A primal and dual sparse approach to extreme multiclass and multilabel classification
I. E. Yen, X. Huang, P. Ravikumar, K. Zhong, and I. S. Dhillon · 2016
Later among the works it cites.
DiSMEC : Distributed sparse machines for extreme multi-label classification
R. Babbar and B. Schölkopf · 2017
Later among the works it cites.
Parseval networks: Improving robustness to adversarial examples
M. Cisse, P. Bojanowski, E. Grave, Y. Dauphin, and N. Usunier · 2017
Later among the works it cites.
A probabilistic framework for zero-shot multi-label learning
A. Gaure, A. Gupta, V. K. Verma, and P. Rai · 2017
Later among the works it cites.
Scalable generative models for multi-label learning with missing labels
V. Jain, N. Modhe, and P. Rai · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
User conditional hashtag prediction for images
E. Denton, J. Weston, M. Paluri, L. Bourdev, and R. Fergus · 2015
Cited alongside, same era.
U. Shaham, Y. Yamada, and S. Negahban · 2015
Cited alongside, same era.
TerseSVM : A scalable approach for learning compact models in large-scale classification
R. Babbar, K. Muandet, and B. Schoelkopf · 2016
Cited alongside, same era.
Learning taxonomy adaptation in large-scale classification
R. Babbar, I. Partalas, E. Gaussier, M.-R. Amini, and C. Amblard · 2016
Cited alongside, same era.
The extreme classification repository: Multi-label datasets and code
K. Bhatia, K. Dahiya, H. Jain, Y. Prabhu, and M. Varma · 2016
Cited alongside, same era.
Logarithmic time one-against-some
H. Daume III, N. Karampatziakis, J. Langford, and P. Mineiro · 2016
Cited alongside, same era.
Extreme multi-label loss functions for recommendation, tagging, ranking and other missing label applications
H. Jain, Y. Prabhu, and M. Varma · 2016
Cited alongside, same era.
Later among the works it cites.
Adversarial examples for evaluating reading comprehension systems
R. Jia and P. Liang · 2017
Later among the works it cites.
Generalization error bounds for extreme multi-class classification
Y. Lei, Ü. Dogan, D. Zhou, and M. Kloft · 2017
Later among the works it cites.
Deep learning for extreme multi-label text classification
J. Liu, W.-C. Chang, Y. Wu, and Y. Yang · 2017
Later among the works it cites.
Label Filters for Large Scale Multilabel Classification
A. Niculescu-Mizil and E. Abbasnejad · 2017
Later among the works it cites.
Subset labeled lda for large-scale multi-label classification
Y. Papanikolaou and G. Tsoumakas · 2017
Later among the works it cites.
Gradient boosted decision trees for high dimensional sparse output
S. Si, H. Zhang, S. S. Keerthi, D. Mahajan, I. S. Dhillon, and C.-J. Hsieh · 2017
Later among the works it cites.
Annexml: Approximate nearest neighbor search for extreme multi-label classification
Y. Tagami · 2017
Later among the works it cites.
Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising
Y. Prabhu, A. Kag, S. Harsola, R. Agrawal, and M. Varma · 2018
Closest in time.