Follow
Flavian Vasile
Flavian Vasile
Criteo
Verified email at criteo.com
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
3132016
Causal embeddings for recommendation
S Bonner, F Vasile
Proceedings of the 12th ACM conference on recommender systems, 104-112, 2018
2842018
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
1902017
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
1722018
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
682010
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
522020
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
472009
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
392020
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
322020
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
262017
Cost-sensitive learning for utility optimization in online advertising auctions
F Vasile, D Lefortier, O Chapelle
Proceedings of the ADKDD'17, 1-6, 2017
232017
On the value of bandit feedback for offline recommender system evaluation
O Jeunen, D Rohde, F Vasile
arXiv preprint arXiv:1907.12384, 2019
172019
TRIPPER: Rule learning using taxonomies
F Vasile, A Silvescu, DK Kang, V Honavar
Advances in Knowledge Discovery and Data Mining: 10th Pacific-Asia …, 2006
162006
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
152020
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
152019
Recommendation system-based upper confidence bound for online advertising
N Nguyen-Thanh, D Marinca, K Khawam, D Rohde, F Vasile, ES Lohan, ...
arXiv preprint arXiv:1909.04190, 2019
142019
Relaxed softmax for PU learning
U Tanielian, F Vasile
Proceedings of the 13th ACM Conference on Recommender Systems, 119-127, 2019
122019
Siamese cookie embedding networks for cross-device user matching
U Tanielian, AM Tousch, F Vasile
Companion Proceedings of the The Web Conference 2018, 85-86, 2018
112018
Improving offline contextual bandits with distributional robustness
O Sakhi, L Faury, F Vasile
arXiv preprint arXiv:2011.06835, 2020
102020
Three methods for training on bandit feedback
D Mykhaylov, D Rohde, F Vasile, M Bompaire, O Jeunen
arXiv preprint arXiv:1904.10799, 2019
102019
The system can't perform the operation now. Try again later.
Articles 1–20