Masrour Zoghi
Masrour Zoghi
Google Research
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Zitiert von
Zitiert von
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
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
Regret bounds for deterministic Gaussian process bandits
N de Freitas, A Smola, M Zoghi
arXiv preprint arXiv:1203.2177, 2012
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
Contextual dueling bandits
M Dudík, K Hofmann, RE Schapire, A Slivkins, M Zoghi
Conference on Learning Theory, 563-587, 2015
Copeland dueling bandits
M Zoghi, ZS Karnin, S Whiteson, M De Rijke
Advances in Neural Information Processing Systems, 307-315, 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
Relative Confidence Sampling for Efficient On-Line Ranker Evaluation
M Zoghi, S Whiteson, M de Rijke, R Munos
Advancements in Dueling Bandits.
Y Sui, M Zoghi, K Hofmann, Y Yue
IJCAI, 5502-5510, 2018
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
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
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
Instance-dependent regret bounds for dueling bandits
A Balsubramani, Z Karnin, RE Schapire, M Zoghi
Conference on Learning Theory, 336-360, 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
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
The Gromov width of coadjoint orbits of compact Lie groups
M Zoghi
University of Toronto, 2010
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
Using confidence bounds for efficient on-line ranker evaluation
M Zoghi, S Whiteson, M de Rijke, R Munos
WSDM 14, 24, 2014
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
An effective orbifold groupoid is determined up to Morita equivalence by its underlying diffeological orbifold
Y Karshon, M Zoghi
preprint, 2008
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