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We study the problem of batch learning from bandit feedback in the setting of extremely large action spaces.
Fonctions de repartition a n-dimensions et leurs marges
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Rao-Blackwellisation of sampling schemes
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Exploration scavenging
Langford, J.; Strehl, A.; and Wortman, J. 2008 · 2008
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Efficient pairwise multilabel classification for large-scale problems in the legal domain
Mencia, E. L.; and Fürnkranz, J. 2008 · 2008
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Doubly Robust Policy Evaluation and Learning
Dudik, M.; Langford, J.; and Li, L. 2011 · 2011
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Off-policy actor-critic
Degris, T.; White, M.; and Sutton, R. S. 2012 · 2012
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Enhancing navigation on Wikipedia with social tags
Zubiaga, A. 2012 · 2012
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Multi-label learning with millions of labels: Recommending advertiser bid phrases for web pages
Agrawal, R.; Gupta, A.; Prabhu, Y.; and Varma, M. 2013 · 2013
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Hidden factors and hidden topics: understanding rating dimensions with review text
McAuley, J.; and Leskovec, J. 2013 · 2013
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Distributed representations of words and phrases and their compositionality
Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. 2013 · 2013
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On multilabel classification and ranking with bandit feedback
Gentile, C.; and Orabona, F. 2014 · 2014
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Sparse local embeddings for extreme multi-label classification
Bhatia, K.; Jain, H.; Kar, P.; Varma, M.; and Jain, P. 2015 · 2015
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Toward predicting the outcome of an A/B experiment for search relevance
Li, L.; Kim, J. Y.; and Zitouni, I. 2015 · 2015
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The extreme classification repository: Multi-label datasets and code
Bhatia, K.; Dahiya, K.; Jain, H.; Mittal, A.; Prabhu, Y.; and Varma, M. 2016 · 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 · 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 · 2016
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Learning Representations for Counterfactual Inference
Johansson, F.; Shalit, U.; and Sontag, D. 2016 · 2016
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Large-scale Validation of Counterfactual Learning Methods: A Test-Bed
Lefortier, D.; Swaminathan, A.; Gu, X.; Joachims, T.; and de Rijke, M. 2016 · 2016
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Beyond ranking: Optimizing whole-page presentation
Wang, Y.; Yin, D.; Jie, L.; Wang, P.; Yamada, M.; Chang, Y.; and Mei, Q. 2016 · 2016
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Pd-sparse: A primal and dual sparse approach to extreme multiclass and multilabel classification
Yen, I. E.-H.; Huang, X.; Ravikumar, P.; Zhong, K.; and Dhillon, I. 2016 · 2016
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Constrained Policy Optimization
Offline evaluation of ranking policies with click models
Li, S.; Abbasi-Yadkori, Y.; Kveton, B.; Muthukrishnan, S.; Vinay, V.; and Wen, Z. 2018 · 2018
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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 · 2018
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Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization
Wu, H.; and Wang, M. 2018 · 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 · 2018
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Data scarcity, robustness and extreme multi-label classification
Babbar, R.; and Schölkopf, B. 2019 · 2019
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Achiam, J.; Held, D.; Tamar, A.; and Abbeel, P. 2017 · 2017
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Dismec: Distributed sparse machines for extreme multi-label classification
Babbar, R.; and Schölkopf, B. 2017 · 2017
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Unbiased learning-to-rank with biased feedback
Joachims, T.; Swaminathan, A.; and Schnabel, T. 2017 · 2017
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Deep learning for extreme multi-label text classification
Liu, J.; Chang, W.-C.; Wu, Y.; and Yang, Y. 2017 · 2017
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Off-policy evaluation for slate recommendation
Swaminathan, A.; Krishnamurthy, A.; Agarwal, A.; Dudik, M.; Langford, J.; Jose, D.; and Zitouni, I. 2017 · 2017
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AnnexML: Approximate nearest neighbor search for extreme multi-label classification
Tagami, Y. 2017 · 2017
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Ppdsparse: A parallel primal-dual sparse method for extreme classification
Yen, I. E.-H.; Huang, X.; Dai, W.; Ravikumar, P.; Dhillon, I.; and Xing, E. 2017 · 2017
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A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation
Bai, X.; Guan, J.; and Wang, H. 2019 · 2019
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X-BERT: eXtreme Multi-label Text Classification with using Bidirectional Encoder Representations from Transformers
Chang, W.-C.; Yu, H.-F.; Zhong, K.; Yang, Y.; and Dhillon, I. 2019 · 2019
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Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces
Guo, C.; Mousavi, A.; Wu, X.; Holtmann-Rice, D.; Kale, S.; Reddi, S.; and Kumar, S. 2019 · 2019
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A Review of Trends and Techniques in Recommender Systems
Rahul; Dahiya, H.; and Singh, D. 2019 · 2019
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CAB: Continuous adaptive blending for policy evaluation and learning
Su, Y.; Wang, L.; Santacatterina, M.; and Joachims, T. 2019 · 2019
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Batch Learning from Bandit Feedback through Bias Corrected Reward Imputation
Wang, L.; Bai, Y.; Bhalla, A.; and Joachims, T. 2019 · 2019
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Bonsai-diverse and shallow trees for extreme multi-label classification
Khandagale, S.; Xiao, H.; and Babbar, R. 2020 · 2020
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Cost-Effective Incentive Allocation via Structured Counterfactual Inference
Lopez, R.; Li, C.; Yan, X.; Xiong, J.; Jordan, M.; Qi, Y.; and Song, L. 2020 · 2020
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Extreme Regression for Dynamic Search Advertising
Prabhu, Y.; Kusupati, A.; Gupta, N.; and Varma, M. 2020 · 2020
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