| Probabilistic Feature Selection and Classification Vector Machine B Jiang, C Li, H Chen, X Yao, M de Rijke ACM Transactions on Knowledge Discovery from Data 13 (2), Article 21, 2019 | 50 | 2019 |
| Cascading hybrid bandits: Online learning to rank for relevance and diversity C Li, H Feng, M Rijke Proceedings of the 14th ACM Conference on Recommender Systems, 33-42, 2020 | 39 | 2020 |
| BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback C Li, B Kveton, T Lattimore, I Markov, M de Rijke, C Szepesvári, M Zoghi UAI 2019: Conference on Uncertainty in Artificial Intelligence, 2019 | 39* | 2019 |
| Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model C Li, M de Rijke IJCAI 2019: Twenty-Eighth International Joint Conference on Artificial …, 2019 | 22 | 2019 |
| MergeDTS: A method for effective large-scale online ranker evaluation C Li, I Markov, MD Rijke, M Zoghi ACM Transactions on Information Systems (TOIS) 38 (4), 1-28, 2020 | 20* | 2020 |
| Incremental sparse Bayesian ordinal regression C Li, M de Rijke Neural Networks 106, 294-302, 2018 | 10 | 2018 |
| Sparse Bayesian approach for feature selection C Li, H Chen 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD), 1-7, 2014 | 10 | 2014 |
| Federated unbiased learning to rank C Li, H Ouyang arXiv preprint arXiv:2105.04761, 2021 | 4 | 2021 |
| Online learning to rank with list-level feedback for image filtering C Li, A Grotov, I Markov, M de Rijke arXiv preprint arXiv:1812.04910, 2018 | 3 | 2018 |
| Optimizing ranking systems online as bandits C Li arXiv preprint arXiv:2110.05807, 2021 | | 2021 |
Maarten de RijkeUniversity of Amsterdam & ICAIZweryfikowany adres z uva.nl