Peer Reviewed Publications:
2019
- Learning Nonsymmetric Determinantal Point Processes, Mike Gartrell, Victor-Emmanuel Brunel, Elvis Dohmatob, Syrine Krichene, NeurIPS 2019
- SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits, Etienne Boursier, Vianney Perchet, NeurIPS 2019
- Categorized Bandits, Matthieu Jedor, Vianney Perchet, Jonathan Louedec, NeurIPS 2019
- Singleshot : a scalable approach for tucker decomposition, Abraham Traore, Maxime Berar, Alain Rakotomamonjy, NeurIPS 2019
- Singleshot : a scalable approach for tucker decomposition, Mokhtar Z. Alaya, Maxime Berar, Gilles Gasso, Alain Rakotomamonjy, NeurIPS 2019
- Domain adaptation in Display advertising, Karan Agarwal, Pranjul Yadav and Sathiya Keerthi Selvaraj, RecSys 2019
- Relaxed Softmax for PU Learning, Ugo Tanielian and Flavian Vasile, RecSys 2019
- Tensorized Determinantal Point Processes for Recommendation, M. Gartrell, R. Warlop and J. Mary, KDD 2019
- Limitations of Adversarial Robustness, E. Dohmatob, ICML 2019
- Learning to Bid in Revenue Maximizing Auctions, T.Nedelec, N. El Karoui and V. Perchet, ICML 2019
- Fairness-Aware Learning for Continuous Attributes and Treatments, J. Mary, C. Calauzenes and N. El Karoui, ICML 2019
- EstImAgg: A Learning Framework for Groupwise Aggregated Data, A.Bhowmik, M.Chen, Z.Xing, S.Rajan, SDM 2019
- Learning Determinantal Point Processes by Corrective Negative Sampling, Z. Mariet, M.Gartrell, S.Sra, AISTATS 2019
- Bridging the gap between regret minimization and best arm identification, with application to A/B tests, R.Degenne, T.Nedelec, C.Calauzenes, V.Perchet, AISTATS 2019
2018
- Off-policy learning for Causal Advertising, E. Diemert, A. Heliou and C. Renaudin, Causal Learning Workshop, NIPS 2018
- A Bayesian Solution to the M-Bias Problem, D. Rhode, Causal Learning Workshop, NIPS 2018
- Deep Determinantal Point Processes, M.Gartrell and E. Dohmatob, Relational Representation Learning Workshop, NIPS 2018
- Learning DPPs by Sampling Inferred Negatives, Z. Mariet, M. Gartrell and S. Sra, Relational Representation Learning Workshop, NIPS 2018
- Reacting to Variations in Product Demand: An Application for Conversion Rate (CR) Prediction in Sponsored Search, M. Tallis and P.Yadav, IEEE Big Data, 2018
- Causal Embeddings for Recommendation, S.Bonner and F.Vasile, RecSys 2018 (Best Paper)
- RecoGym: A Reinforcement Learning Environment for the problem of Product Recommendation in Online Advertising, D.Rhode, S.Bonner, T. Dunlop, F.Vasile and A. Karatzoglou, REVEAL Workshop, RecSys 2018
- Anonymous Walk Embeddings, S. Ivanov, ICML 2018
- Improved Regret Bounds for Thompson Sampling in LQ Problems, M. Abeille, ICML 2018
- Naive Parallelization of Coordinate Descent Methods and an Application on Multi-core L1-regularized Classification, Y. Zhuang, Y. Juan, G. Yuan & C. Lin, CIKM 2018
- A Large Scale Benchmark for Uplift Modeling, E. Diemert, A. Betlei, C. Renaudin & MR. Amini, AdKDD & TargetAd Workshop, KDD 2018 (dataset)
- SpectralWords: Spectral Embeddings Approach to Word Similarity Task for Large Vocabularies, I.Lobov, ICLR 2018
- Learning Time/Memory-Efficient Deep Architectures with Budgeted Super Networks, T. Veniat & L. Denoyer, CVPR 2018
- Multi-View Data Generation Without View Supervision, M. Chen, L. Denoyer & T. Artières, ICLR 2018
- Siamese Cookie Embedding Networks for Cross-Device User Matching, U. Tanielian, A. Tousch & F. Vasile, WWW 2018
- Offline A/B testing for recommender system, A. Gilotte, C. Calauzenes, T. Nedelec, A. Abraham & S. Dolle, WSDM 2018 (Best Paper Honorable Mention)
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
