| Survey on applications of multi-armed and contextual bandits D Bouneffouf, I Rish, C Aggarwal 2020 IEEE Congress on Evolutionary Computation (CEC), 1-8, 2020 | 410* | 2020 |
| A contextual-bandit algorithm for mobile context-aware recommender system D Bouneffouf, A Bouzeghoub, AL Gançarski International conference on neural information processing, 324-331, 2012 | 199 | 2012 |
| A neural networks committee for the contextual bandit problem R Allesiardo, R Féraud, D Bouneffouf Neural Information Processing: 21st International Conference, ICONIP 2014 …, 2014 | 140* | 2014 |
| Data augmentation for discrimination prevention and bias disambiguation S Sharma, Y Zhang, JM Ríos Aliaga, D Bouneffouf, V Muthusamy, ... Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 358-364, 2020 | 128 | 2020 |
| Multi-armed bandit problem with known trend D Bouneffouf, R Féraud Neurocomputing 205, 16-21, 2016 | 117 | 2016 |
| Teaching AI agents ethical values using reinforcement learning and policy orchestration R Noothigattu, D Bouneffouf, N Mattei, R Chandra, P Madan, ... IBM Journal of Research and Development 63 (4/5), 2: 1-2: 9, 2019 | 106 | 2019 |
| Contextual bandit for active learning: Active thompson sampling D Bouneffouf, R Laroche, T Urvoy, R Féraud, R Allesiardo Neural Information Processing: 21st International Conference, ICONIP 2014 …, 2014 | 103 | 2014 |
| International conference on neural information processing X Guo, X Liu, E Zhu, J Yin Springer, Cham,, 2017 | 102 | 2017 |
| Context attentive bandits: Contextual bandit with restricted context D Bouneffouf, I Rish, GA Cecchi, R Féraud IJCAI 2017, 2017 | 97 | 2017 |
| An ADMM based framework for automl pipeline configuration S Liu, P Ram, D Vijaykeerthy, D Bouneffouf, G Bramble, H Samulowitz, ... Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 4892-4899, 2020 | 87 | 2020 |
| Beyond backprop: Online alternating minimization with auxiliary variables A Choromanska, B Cowen, S Kumaravel, R Luss, M Rigotti, I Rish, ... International Conference on Machine Learning, 1193-1202, 2019 | 87* | 2019 |
| Incorporating behavioral constraints in online AI systems A Balakrishnan, D Bouneffouf, N Mattei, F Rossi Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3-11, 2019 | 84 | 2019 |
| Scalable recollections for continual lifelong learning M Riemer, T Klinger, D Bouneffouf, M Franceschini Proceedings of the AAAI conference on artificial intelligence 33 (01), 1352-1359, 2019 | 82 | 2019 |
| Using Contextual Bandits with Behavioral Constraints for Constrained Online Movie Recommendation. A Balakrishnan, D Bouneffouf, N Mattei, F Rossi IJCAI, 5802-5804, 2018 | 66 | 2018 |
| Following the user's interests in mobile context-aware recommender systems: The hybrid-E-greedy algorithm D Bouneffouf, A Bouzeghoub, AL Gançarski 2012 26th International Conference on Advanced Information Networking and …, 2012 | 66 | 2012 |
| Hybrid-ε-greedy for Mobile Context-Aware Recommender System D Bouneffouf, A Bouzeghoub, AL Gançarski Advances in Knowledge Discovery and Data Mining: 16th Pacific-Asia …, 2012 | 64* | 2012 |
| A story of two streams: Reinforcement learning models from human behavior and neuropsychiatry B Lin, G Cecchi, D Bouneffouf, J Reinen, I Rish AAMAS, 2019 | 52 | 2019 |
| Exponentiated gradient exploration for active learning D Bouneffouf Computers 5 (1), 1, 2016 | 50 | 2016 |
| Risk-aware recommender systems D Bouneffouf, A Bouzeghoub, AL Ganarski Neural Information Processing: 20th International Conference, ICONIP 2013 …, 2013 | 50 | 2013 |
| Optimal exploitation of clustering and history information in multi-armed bandit D Bouneffouf, S Parthasarathy, H Samulowitz, M Wistub arXiv preprint arXiv:1906.03979, 2019 | 49 | 2019 |
Irina RishUniversity of Montreal / Mila -Quebec AI InstituteVerified email at mila.quebec
