Lucas Maystre
Lucas Maystre
Research Scientist, Spotify
Verified email at spotify.com - Homepage
Title
Cited by
Cited by
Year
Fast and accurate inference of plackett–luce models
L Maystre, M Grossglauser
Advances in neural information processing systems 28, 172-180, 2015
582015
Just sort it! A simple and effective approach to active preference learning
L Maystre, M Grossglauser
Proceedings of ICML 2017, 2017
34*2017
Collaborative recurrent neural networks for dynamic recommender systems
YJ Ko, L Maystre, M Grossglauser
Journal of Machine Learning Research: Workshop and Conference Proceedings 63†…, 2016
312016
Mitigating epidemics through mobile micro-measures
M Kafsi, E Kazemi, L Maystre, L Yartseva, M Grossglauser, P Thiran
arXiv preprint arXiv:1307.2084, 2013
132013
ChoiceRank: Identifying Preferences from Node Traffic in Networks
L Maystre, M Grossglauser
Proceedings of ICML 2017, 2017
72017
The player kernel: learning team strengths based on implicit player contributions
L Maystre, V Kristof, AJG Ferrer, M Grossglauser
arXiv preprint arXiv:1609.01176, 2016
62016
Can Who-Edits-What Predict Edit Survival?
AB Yardim, V Kristof, L Maystre, M Grossglauser
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge†…, 2018
22018
Algorithmic effects on the diversity of consumption on spotify
A Anderson, L Maystre, I Anderson, R Mehrotra, M Lalmas
Proceedings of The Web Conference 2020, 2155-2165, 2020
12020
A User Study of Perceived Carbon Footprint
V Kristof, V Quelquejay-LeclŤre, R Zbinden, L Maystre, M Grossglauser, ...
arXiv preprint arXiv:1911.11658, 2019
2019
Pairwise Comparisons with Flexible Time-Dynamics
L Maystre, V Kristof, M Grossglauser
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge†…, 2019
2019
Scalable and Efficient Comparison-based Search without Features
D Chumbalov, L Maystre, M Grossglauser
arXiv preprint arXiv:1905.05049, 2019
2019
Efficient Learning from Comparisons
L Maystre
EPFL, 2018
2018
Music Recommendation to Groups
L Maystre
2012
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Articles 1–13