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Flavian Vasile
Flavian Vasile
Criteo
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Title
Cited by
Cited by
Year
Meta-prod2vec: Product embeddings using side-information for recommendation
F Vasile, E Smirnova, A Conneau
Proceedings of the 10th ACM conference on recommender systems, 225-232, 2016
2172016
Contextual sequence modeling for recommendation with recurrent neural networks
E Smirnova, F Vasile
Proceedings of the 2nd workshop on deep learning for recommender systems, 2-9, 2017
1472017
Causal embeddings for recommendation
S Bonner, F Vasile
Proceedings of the 12th ACM conference on recommender systems, 104-112, 2018
1302018
Recogym: A reinforcement learning environment for the problem of product recommendation in online advertising
D Rohde, S Bonner, T Dunlop, F Vasile, A Karatzoglou
arXiv preprint arXiv:1808.00720, 2018
982018
Resolving surface forms to wikipedia topics
Y Zhou, L Nie, O Rouhani-Kalleh, F Vasile, S Gaffney
Proceedings of the 23rd International Conference on Computational …, 2010
632010
Learning a named entity tagger from gazetteers with the partial perceptron.
A Carlson, S Gaffney, F Vasile
AAAI Spring Symposium: Learning by Reading and Learning to Read, 7-13, 2009
392009
Specializing joint representations for the task of product recommendation
T Nedelec, E Smirnova, F Vasile
Proceedings of the 2nd workshop on deep learning for recommender systems, 10-18, 2017
242017
Distributionally robust counterfactual risk minimization
L Faury, U Tanielian, E Dohmatob, E Smirnova, F Vasile
Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3850-3857, 2020
232020
Blob: A probabilistic model for recommendation that combines organic and bandit signals
O Sakhi, S Bonner, D Rohde, F Vasile
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
162020
Joint policy-value learning for recommendation
O Jeunen, D Rohde, F Vasile, M Bompaire
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge …, 2020
162020
TRIPPER: Rule learning using taxonomies
F Vasile, A Silvescu, DK Kang, V Honavar
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 55-59, 2006
152006
Cost-sensitive learning for utility optimization in online advertising auctions
F Vasile, D Lefortier, O Chapelle
Proceedings of the ADKDD'17, 1-6, 2017
142017
On the value of bandit feedback for offline recommender system evaluation
O Jeunen, D Rohde, F Vasile
arXiv preprint arXiv:1907.12384, 2019
102019
Learning from Bandit Feedback: An Overview of the State-of-the-art
O Jeunen, D Mykhaylov, D Rohde, F Vasile, A Gilotte, M Bompaire
arXiv preprint arXiv:1909.08471, 2019
92019
Relaxed softmax for PU learning
U Tanielian, F Vasile
Proceedings of the 13th ACM Conference on Recommender Systems, 119-127, 2019
82019
Cost-sensitive learning for bidding in online advertising auctions
F Vasile, D Lefortier
CoRR arXiv 1603, 2016
72016
Three methods for training on bandit feedback
D Mykhaylov, D Rohde, F Vasile, M Bompaire, O Jeunen
arXiv preprint arXiv:1904.10799, 2019
62019
REVEAL 2018: offline evaluation for recommender systems
T Joachims, A Swaminathan, Y Raimond, O Koch, F Vasile
Proceedings of the 12th ACM Conference on Recommender Systems, 514-515, 2018
62018
RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising. ArXiv e-prints (Aug
D Rohde, S Bonner, T Dunlop, F Vasile, A Karatzoglou
arXiv preprint arXiv:1808.00720, 2018
62018
A gentle introduction to recommendation as counterfactual policy learning
F Vasile, D Rohde, O Jeunen, A Benhalloum
Proceedings of the 28th ACM Conference on User Modeling, Adaptation and …, 2020
52020
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