| Bayesian optimization in a billion dimensions via random embeddings Z Wang, F Hutter, M Zoghi, D Matheson, N De Feitas Journal of Artificial Intelligence Research 55, 361-387, 2016 | 753 | 2016 |
| Regret bounds for deterministic Gaussian process bandits N de Freitas, A Smola, M Zoghi arXiv preprint arXiv:1203.2177, 2012 | 141* | 2012 |
| Relative Upper Confidence Bound For The K-Armed Dueling Bandit Problem M Zoghi, S Whiteson, R Munos, M de Rijke Proceedings of The 31st International Conference on Machine Learning 32, 10-18, 2014 | 140 | 2014 |
| Online learning to rank in stochastic click models M Zoghi, T Tunys, M Ghavamzadeh, B Kveton, C Szepesvari, Z Wen International conference on machine learning, 4199-4208, 2017 | 99 | 2017 |
| Copeland dueling bandits M Zoghi, ZS Karnin, S Whiteson, M De Rijke Advances in Neural Information Processing Systems, 307-315, 2015 | 94 | 2015 |
| Contextual dueling bandits M Dudík, K Hofmann, RE Schapire, A Slivkins, M Zoghi Conference on Learning Theory, 563-587, 2015 | 81 | 2015 |
| Revisiting approximate metric optimization in the age of deep neural networks S Bruch, M Zoghi, M Bendersky, M Najork Proceedings of the 42nd international ACM SIGIR conference on research and …, 2019 | 62 | 2019 |
| Relative Confidence Sampling for Efficient On-Line Ranker Evaluation M Zoghi, S Whiteson, M de Rijke, R Munos | 59* | |
| MergeRUCB: A method for large-scale online ranker evaluation M Zoghi, S Whiteson, M de Rijke Proceedings of the Eighth ACM International Conference on Web Search and …, 2015 | 58 | 2015 |
| Advancements in Dueling Bandits. Y Sui, M Zoghi, K Hofmann, Y Yue IJCAI, 5502-5510, 2018 | 56 | 2018 |
| 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 Uncertainty in artificial intelligence, 196-206, 2020 | 37* | 2020 |
| Click-based hot fixes for underperforming torso queries M Zoghi, T Tunys, L Li, D Jose, J Chen, CM Chin, M de Rijke Proceedings of the 39th International ACM SIGIR conference on Research and …, 2016 | 33 | 2016 |
| Google COVID-19 search trends symptoms dataset: Anonymization process description (version 1.0) S Bavadekar, A Dai, J Davis, D Desfontaines, I Eckstein, K Everett, ... arXiv preprint arXiv:2009.01265, 2020 | 14 | 2020 |
| Instance-dependent regret bounds for dueling bandits A Balsubramani, Z Karnin, RE Schapire, M Zoghi Conference on Learning Theory, 336-360, 2016 | 14 | 2016 |
| The Gromov width of coadjoint orbits of compact Lie groups M Zoghi University of Toronto, 2010 | 14 | 2010 |
| 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 | 13 | 2020 |
| On the value of prior in online learning to rank B Kveton, O Meshi, M Zoghi, Z Qin International Conference on Artificial Intelligence and Statistics, 6880-6892, 2022 | 7 | 2022 |
| Merge double Thompson sampling for large scale online ranker evaluation C Li, I Markov, M de Rijke, M Zoghi arXiv preprint arXiv:1812.04412, 2018 | 5 | 2018 |
| Using confidence bounds for efficient on-line ranker evaluation M Zoghi, S Whiteson, M de Rijke, R Munos WSDM 14, 24, 2014 | 5 | 2014 |
| An effective orbifold groupoid is determined up to Morita equivalence by its underlying diffeological orbifold Y Karshon, M Zoghi preprint, 2008 | 4 | 2008 |
Maarten de RijkeUniversity of Amsterdam & ICAIVerified email at uva.nl
