Lucas Maystre

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 35

Top Topics

Observational Data

Preference Learning

Top Venues

Publications

Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek
On the Importance of Uncertainty in Decision-Making with Large Language Models. Trans. Mach. Learn. Res. 2024 (2024)
Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek
On the Importance of Uncertainty in Decision-Making with Large Language Models. CoRR (2024)
Matthew Smith, Lucas Maystre, Zhenwen Dai, Kamil Ciosek
A Strong Baseline for Batch Imitation Learning. CoRR (2023)
Buket Baran, Guilherme Dinis Junior, Antonina Danylenko, Olayinka S. Folorunso, Gösta Forsum, Maksym Lefarov, Lucas Maystre, Yu Zhao
Accelerating Creator Audience Building through Centralized Exploration. RecSys (2023)
Lucas Maystre, Daniel Russo, Yu Zhao
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective. CoRR (2023)
Graham Van Goffrier, Lucas Maystre, Ciarán M. Gilligan-Lee
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding. CoRR (2023)
Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. CoRR (2023)
Graham Van Goffrier, Lucas Maystre, Ciarán Mark Gilligan-Lee
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding. CLeaR (2023)
Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. KDD (2023)
Daniyar Chumbalov, Lars Klein, Lucas Maystre, Matthias Grossglauser
Fast Interactive Search with a Scale-Free Comparison Oracle. CoRR (2023)
Lucas Maystre, Daniel Russo
Temporally-Consistent Survival Analysis. NeurIPS (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. WWW (2022)
Lucas Maystre, Tiffany Wu, Roberto Sanchis-Ojeda, Tony Jebara
Multistate analysis with infinite mixtures of Markov chains. UAI (2022)
Lillio Mok, Samuel F. Way, Lucas Maystre, Ashton Anderson
The Dynamics of Exploration on Spotify. ICWSM (2022)
Francesco Sanna Passino, Lucas Maystre, Dmitrii Moor, Ashton Anderson, Mounia Lalmas
Where To Next? A Dynamic Model of User Preferences. WWW (2021)
Judith Bütepage, Lucas Maystre, Mounia Lalmas
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty. ECML/PKDD (2) (2021)
Christian Hansen, Rishabh Mehrotra, Casper Hansen, Brian Brost, Lucas Maystre, Mounia Lalmas
Shifting Consumption towards Diverse Content on Music Streaming Platforms. WSDM (2021)
Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas
Collaborative Classification from Noisy Labels. AISTATS (2021)
Ashton Anderson, Lucas Maystre, Ian Anderson, Rishabh Mehrotra, Mounia Lalmas
Algorithmic Effects on the Diversity of Consumption on Spotify. WWW (2020)
Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser
Scalable and Efficient Comparison-based Search without Features. ICML (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. RecSys (2020)
Victor Kristof, Valentin Quelquejay-Leclère, Robin Zbinden, Lucas Maystre, Matthias Grossglauser, Patrick Thiran
A User Study of Perceived Carbon Footprint. CoRR (2019)
Lucas Maystre, Victor Kristof, Matthias Grossglauser
Pairwise Comparisons with Flexible Time-Dynamics. KDD (2019)
Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser
Learning to Search Efficiently Using Comparisons. CoRR (2019)
Lucas Maystre, Victor Kristof, Matthias Grossglauser
Linear-Time Inference for Pairwise Comparisons with Gaussian-Process Dynamics. CoRR (2019)
Ali Batuhan Yardim, Victor Kristof, Lucas Maystre, Matthias Grossglauser
Can Who-Edits-What Predict Edit Survival? CoRR (2018)
Ali Batuhan Yardim, Victor Kristof, Lucas Maystre, Matthias Grossglauser
Can Who-Edits-What Predict Edit Survival? KDD (2018)
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. ICML (2017)
Lucas Maystre, Victor Kristof, Antonio J. González Ferrer, Matthias Grossglauser
The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions. CoRR (2016)
Young-Jun Ko, Lucas Maystre, Matthias Grossglauser
Collaborative Recurrent Neural Networks for Dynamic Recommender Systems. ACML (2016)
Lucas Maystre, Matthias Grossglauser
ChoiceRank: Identifying Preferences from Node Traffic in Networks. CoRR (2016)
Lucas Maystre, Victor Kristof, Antonio J. González Ferrer, Matthias Grossglauser
The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions. MLSA@PKDD/ECML (2016)
Lucas Maystre, Matthias Grossglauser
Fast and Accurate Inference of Plackett-Luce Models. NIPS (2015)
Lucas Maystre, Matthias Grossglauser
Robust Active Ranking from Sparse Noisy Comparisons. CoRR (2015)
Mohamed Kafsi, Ehsan Kazemi, Lucas Maystre, Lyudmila Yartseva, Matthias Grossglauser, Patrick Thiran
Mitigating Epidemics through Mobile Micro-measures. CoRR (2013)