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
1452016
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
852017
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
622010
Causal embeddings for recommendation
S Bonner, F Vasile
Proceedings of the 12th ACM Conference on Recommender Systems, 104-112, 2018
402018
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
332009
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
292018
Toward controlling discrimination in online ad auctions
LE Celis, A Mehrotra, NK Vishnoi
arXiv preprint arXiv:1901.10450, 2019
16*2019
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
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
132017
Cost-sensitive learning for utility optimization in online advertising auctions
F Vasile, D Lefortier, O Chapelle
Proceedings of the ADKDD'17, 1-6, 2017
102017
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 cs.IR/1808.00720, 2018
52018
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
42019
Three Methods for Training on Bandit Feedback
D Mykhaylov, D Rohde, F Vasile, M Bompaire, O Jeunen
arXiv preprint arXiv:1904.10799, 2019
42019
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
42018
Cost-sensitive learning for bidding in online advertising auctions
F Vasile, D Lefortier
CoRR, 2016
42016
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
32020
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
32020
On the Value of Bandit Feedback for Offline Recommender System Evaluation
O Jeunen, D Rohde, F Vasile
arXiv preprint arXiv:1907.12384, 2019
32019
Distributionally Robust Counterfactual Risk Minimization
L Faury, U Tanielian, F Vasile, E Smirnova, E Dohmatob
arXiv preprint arXiv:1906.06211, 2019
32019
Relaxed softmax for PU learning
U Tanielian, F Vasile
Proceedings of the 13th ACM Conference on Recommender Systems, 119-127, 2019
22019
The system can't perform the operation now. Try again later.
Articles 1–20