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Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S., McAllester, D. A., Singh, S. P., and Mansour, Y · 2000
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Item-based collaborative filtering recommendation algorithms
Sarwar, B., Karypis, G., Konstan, J., and Riedl, J · 2001
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Learning as search optimization: Approximate large margin methods for structured prediction
Daumé III, H. and Marcu, D · 2005
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Hierarchical probabilistic neural network language model
Morin, F. and Bengio, Y · 2005
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On learning linear ranking functions for beam search
Xu, Y. and Fern, A · 2007
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A reduction of imitation learning and structured prediction to no-regret online learning
Ross, S., Gordon, G., and Bagnell, D · 2011
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Fastxml: A fast, accurate and stable tree-classifier for extreme multi-label learning
Prabhu, Y. and Varma, M · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Image-based recommendations on styles and substitutes
McAuley, J., Targett, C., Shi, Q., and Van Den Hengel, A · 2015
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Deep neural networks for youtube recommendations
Covington, P., Adams, J., and Sargin, E · 2016
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Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
He, R. and McAuley, J · 2016
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Extreme multi-label loss functions for recommendation, tagging, ranking & other missing label applications
Jain, H., Prabhu, Y., and Varma, M · 2016
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Extreme f-measure maximization using sparse probability estimates
Jasinska, K., Dembczynski, K., Busa-Fekete, R., Pfannschmidt, K., Klerx, T., and Hullermeier, E · 2016
Cited alongside, same era.
Sequence level training with recurrent neural networks
Ranzato, M., Chopra, S., Auli, M., and Zaremba, W · 2016
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Sequence-to-sequence learning as beam-search optimization
Wiseman, S. and Rush, A. M · 2016
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An actor-critic algorithm for sequence prediction
Bahdanau, D., Brakel, P., Xu, K., Goyal, A., Lowe, R., Pineau, J., Courville, A., and Bengio, Y · 2017
Parabel: Partitioned label trees for extreme classification with application to dynamic search advertising
Prabhu, Y., Kag, A., Harsola, S., Agrawal, R., and Varma, M · 2018
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A no-regret generalization of hierarchical softmax to extreme multi-label classification
Wydmuch, M., Jasinska, K., Kuznetsov, M., Busa-Fekete, R., and Dembczynski, K · 2018
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Learning tree-based deep model for recommender systems
Zhu, H., Li, X., Zhang, P., Li, G., He, J., Li, H., and Gai, K · 2018
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Empirical analysis of beam search performance degradation in neural sequence models
Cohen, E. and Beck, C · 2019
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Bonsai-diverse and shallow trees for extreme multi-label classification
Khandagale, S., Xiao, H., and Babbar, R · 2019
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Analysis and optimization of loss functions for multiclass, top-k, and multilabel classification
Lapin, M., Hein, M., and Schiele, B · 2017
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A unified view of multi-label performance measures
Wu, X.-Z. and Zhou, Z.-H · 2017
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A continuous relaxation of beam search for end-to-end training of neural sequence models
Goyal, K., Neubig, G., Dyer, C., and Berg-Kirkpatrick, T · 2018
Cited alongside, same era.
Learning beam search policies via imitation learning
Negrinho, R., Gormley, M., and Gordon, G. J · 2018
Cited alongside, same era.
Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement
Kool, W., Van Hoof, H., and Welling, M · 2019
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Multilabel reductions: what is my loss optimising?
Menon, A. K., Rawat, A. S., Reddi, S., and Kumar, S · 2019
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On the consistency of top-k surrogate losses
Yang, F. and Koyejo, S · 2019
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Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification
You, R., Zhang, Z., Wang, Z., Dai, S., Mamitsuka, H., and Zhu, S · 2019
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Joint optimization of tree-based index and deep model for recommender systems
Zhu, H., Chang, D., Xu, Z., Zhang, P., Li, X., He, J., Li, H., Xu, J., and Gai, K · 2019
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