Publications 2018 — today (automatically extracted from HAL, using Haltools)
Publications before 2017
2017
- A Comparative Study of Counterfactual Estimators, T.Nedelec, N. Le Roux and V. Perchet, What If, What Next Workshop, NIPS 2017
- Attribution Modeling Increases Efficiency of Bidding in Display Advertising, E. Diemert, J. Meynet, P. Galland & D. Lefortier, AdKDD & TargetAd Workshop, KDD 2017 (Best Paper Award Finalist)
- Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions, F. Vasile, D. Lefortier, & O. Chapelle, AdKDD & TargetAd Workshop, KDD 2017
- Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks, E. Smirnova & F. Vasile, Deep Learning Workshop, RecSys 2017
- Specializing Joint Representations for the task of Product Recommendation, T. Nedelec, E. Smirnova & F. Vasile, Deep Learning Workshop, RecSys 2017
- Stochastic Bandit Models for Delayed Conversions, C. Vernade, O. Cappe, V. Perchet, UAI 2017
- Memorizing the Playout Policy, T. Cazenave & E. Diemert, Computer Games Workshop, IJCAI 2017
- Sparse Stochastic Bandits, J. Kwon, V. Perchet & C.Vernade, COLT 2017
- Efficient Vector Representation for Documents through Corruption, M. Chen, ICLR 2017
- Tighter bounds lead to improved classifiers, N. Le Roux, ICLR 2017
- Field-aware Factorization Machines in a Real-world Online Advertising System, Y. Juan, D. Lefortier and O. Chapelle, WWW 2017
2016
- Meta-Prod2Vec – Product Embeddings Using Side-Information for Recommendation, F. Vasile, E. Smirnova and A. Conneau, RecSys 2016
2015
- Cost-sensitive Learning for Bidding in Online Advertising Auctions, F. Vasile and D. Lefortier, NIPS ML for e-Commerce 2015
- Offline evaluation of response prediction in online advertising auctions, O. Chapelle, WWW, 2015
2014
- Modeling delayed feedback in display advertising, O. Chapelle, KDD, 2014
- Classifier cascades and trees for minimizing feature evaluation cost, Z. Xu, M. Kusner, K. Weinberger, M. Chen, and O. Chapelle, JMLR, 15:2113–2144, 2014
Pre-Prints & Internal Reports:
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- A Protocol to Reduce Bias & Variance in Head-to-Head Tests, A. Boyko, Z. Harchaoui, T. Nedelec & V. Perchet, Criteo Internal Report, ID-RSC-162
- Distributed SAGA: Maintaining Linear Convergence Rate with Limited Communication, C. Calauzenes & N. Le Roux, Arxiv, 2017
