Lucas Maystre
Lucas Maystre
Research Scientist, Spotify
Verified email at - Homepage
Cited by
Cited by
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
Fast and accurate inference of Plackett–Luce models
L Maystre, M Grossglauser
NeurIPS 2015, 2015
Contextual and sequential user embeddings for large-scale music recommendation
C Hansen, C Hansen, L Maystre, R Mehrotra, B Brost, F Tomasi, ...
RecSys 2020, 53-62, 2020
Just sort it! A simple and effective approach to active preference learning
L Maystre, M Grossglauser
ICML 2017, 2017
Collaborative recurrent neural networks for dynamic recommender systems
YJ Ko, L Maystre, M Grossglauser
ACML 2016, 2016
Shifting consumption towards diverse content on music streaming platforms
C Hansen, R Mehrotra, C Hansen, B Brost, L Maystre, M Lalmas
Proceedings of the 14th ACM international conference on web search and data …, 2021
Mitigating epidemics through mobile micro-measures
M Kafsi, E Kazemi, L Maystre, L Yartseva, M Grossglauser, P Thiran
arXiv preprint arXiv:1307.2084, 2013
ChoiceRank: Identifying Preferences from Node Traffic in Networks
L Maystre, M Grossglauser
ICML 2017, 2017
Where to next? A dynamic model of user preferences
F Sanna Passino, L Maystre, D Moor, A Anderson, M Lalmas
Proceedings of the Web Conference 2021, 3210-3220, 2021
Pairwise Comparisons with Flexible Time-Dynamics
L Maystre, V Kristof, M Grossglauser
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge …, 2019
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
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
The Dynamics of Exploration on Spotify
L Mok, SF Way, L Maystre, A Anderson
Proceedings of the International AAAI Conference on Web and Social Media 16 …, 2022
Using survival models to estimate user engagement in online experiments
P Chandar, B St. Thomas, L Maystre, V Pappu, R Sanchis-Ojeda, T Wu, ...
Proceedings of the ACM Web Conference 2022, 3186-3195, 2022
Scalable and efficient comparison-based search without features
D Chumbalov, L Maystre, M Grossglauser
ICML 2020, 2020
A strong baseline for batch imitation learning
M Smith, L Maystre, Z Dai, K Ciosek
arXiv preprint arXiv:2302.02788, 2023
Efficient learning from comparisons
L Maystre
EPFL, 2018
Choix—Inference algorithms for models based on Luce’s choice axiom
L Maystre
URL https://github. com/lucasmaystre/choix, 2017
Temporally-consistent survival analysis
L Maystre, D Russo
Advances in Neural Information Processing Systems 35, 10671-10683, 2022
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty
J Bütepage, L Maystre, M Lalmas
ECML-PKDD 2021, 2021
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