Lucas Maystre


Page last edited on 2023-06-08

I strive to make my papers and the corresponding code easily accessible. If you cannot find something, please drop me a line. Note that I also maintain a Google scholar page.

KDD 2023 Thomas McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
CLeaR 2023 Graham Van Goffrier, Lucas Maystre, Ciarán Gilligan-Lee
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding
NeurIPS 2022 Lucas Maystre, Daniel Russo
Temporally-Consistent Survival Analysis
UAI 2022 Lucas Maystre, Tiffany Wu, Roberto Sanchis Ojeda, Tony Jebara
Multistate Analysis with Infinite Mixtures of Markov Chains
ICWSM 2022 Lillio Mok, Samuel F. Way, Lucas Maystre, Ashton Anderson
The Dynamics of Exploration on Spotify
WWW 2022 Praveen Chandar, Brian St. Thomas, Lucas Maystre, Vijay Pappu, Roberto Sanchis-Ojeda, Tiffany Wu, Ben Carterette, Mounia Lalmas, Tony Jebara
Using Survival Models to Estimate User Engagement in Online Experiments
ECML-PKDD 2021 Judith Bütepage, Lucas Maystre, Mounia Lalmas
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty
WWW 2021 Francesco Sanna Passino, Lucas Maystre, Dmitrii Moor, Ashton Anderson, Mounia Lalmas
Where To Next? A Dynamic Model of User Preferences
AISTATS 2021 Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas
Collaborative Classification from Noisy Labels
RecSys 2020 Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas
Contextual and Sequential User Embeddings for Large-Scale Music Recommendation
ICML 2020 Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser
Scalable and Efficient Comparison-based Search without Features
WWW 2020 Ashton Anderson, Lucas Maystre, Rishabh Mehrotra, Ian Anderson, Mounia Lalmas
Algorithmic Effects on the Diversity of Consumption on Spotify
KDD 2019 Lucas Maystre, Victor Kristof, Matthias Grossglauser
Pairwise Comparisons with Flexible Time-Dynamics
KDD 2018 Ali Batuhan Yardım, Victor Kristof, Lucas Maystre, Matthias Grossglauser
Can Who-Edits-What Predict Edit Survival?
ICML 2017 Lucas Maystre, Matthias Grossglauser
Just Sort It! A Simple and Effective Approach to Active Preference Learning
ICML 2017 Lucas Maystre, Matthias Grossglauser
ChoiceRank: Identifying Preferences from Node Traffic in Networks
ACML 2016 Young-Jun Ko, Lucas Maystre, Matthias Grossglauser
Collaborative Recurrent Neural Networks for Dynamic Recommender Systems
NIPS 2015 Lucas Maystre, Matthias Grossglauser
Fast and Accurate Inference of Plackett–Luce Models

Theses, working papers and technical reports.

2023 Lucas Maystre, Daniel Russo, Yu Zhao
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective
2019 Victor Kristof, Valentin Quelquejay-Leclère, Robin Zbinden, Lucas Maystre, Matthias Grossglauser, Patrick Thiran
A User Study of Perceived Carbon Footprint
2018 Lucas Maystre
Efficient Learning from Comparisons
2016 Lucas Maystre, Victor Kristof, Antonio J. González Ferrer, Matthias Grossglauser
The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions
2013 Mohamed Kafsi, Ehsan Kazemi, Lucas Maystre, Lyudmila Yartseva, Matthias Grossglauser, Patrick Thiran
Mitigating Epidemics through Mobile Micro-measures
2012 Lucas Maystre
Music Recommendation to Groups

As part of the GI thesis prize, the introduction of my PhD thesis was translated into German and published in the book Ausgezeichnete Informatikdissertationen 2018.

2019 Lucas Maystre
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