Mitigating Epidemics through Mobile Micro-measures. - Mohamed Kafsi; Ehsan Kazemi 001; Lucas Maystre; Lyudmila Yartseva; Matthias Grossglauser; Patrick Thiran (CoRR, 2013)

Fast and Accurate Inference of Plackett-Luce Models. - Lucas Maystre; Matthias Grossglauser (NIPS, 2015)

Robust Active Ranking from Sparse Noisy Comparisons. - Lucas Maystre; Matthias Grossglauser (CoRR, 2015)

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems. - Young-Jun Ko; Lucas Maystre; Matthias Grossglauser (ACML, 2016)

The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions. - Lucas Maystre; Victor Kristof; Antonio J. González Ferrer; Matthias Grossglauser (MLSA@PKDD/ECML, 2016)

Just Sort It! A Simple and Effective Approach to Active Preference Learning. - Lucas Maystre; Matthias Grossglauser (ICML, 2017)

ChoiceRank: Identifying Preferences from Node Traffic in Networks. - Lucas Maystre; Matthias Grossglauser (ICML, 2017)

Can Who-Edits-What Predict Edit Survival? - Ali Batuhan Yardim; Victor Kristof; Lucas Maystre; Matthias Grossglauser (KDD, 2018)

Efficient Learning from Comparisons. - Lucas Maystre (2018)

Efficient Learning from Comparisons. - Lucas Maystre (Ausgezeichnete Informatikdissertationen, 2018)

Pairwise Comparisons with Flexible Time-Dynamics. - Lucas Maystre; Victor Kristof; Matthias Grossglauser (KDD, 2019)

Linear-Time Inference for Pairwise Comparisons with Gaussian-Process Dynamics. - Lucas Maystre; Victor Kristof; Matthias Grossglauser (CoRR, 2019)

Learning to Search Efficiently Using Comparisons. - Daniyar Chumbalov; Lucas Maystre; Matthias Grossglauser (CoRR, 2019)

A User Study of Perceived Carbon Footprint. - Victor Kristof; Valentin Quelquejay-Leclère; Robin Zbinden; Lucas Maystre; Matthias Grossglauser; Patrick Thiran (CoRR, 2019)

Scalable and Efficient Comparison-based Search without Features. - Daniyar Chumbalov; Lucas Maystre; Matthias Grossglauser (ICML, 2020)

Contextual and Sequential User Embeddings for Large-Scale Music Recommendation. - Casper Hansen; Christian Hansen 004; Lucas Maystre; Rishabh Mehrotra; Brian Brost; Federico Tomasi; Mounia Lalmas (RecSys, 2020)

Algorithmic Effects on the Diversity of Consumption on Spotify. - Ashton Anderson; Lucas Maystre; Ian Anderson 003; Rishabh Mehrotra; Mounia Lalmas (WWW, 2020)

Collaborative Classification from Noisy Labels. - Lucas Maystre; Nagarjuna Kumarappan; Judith Bütepage; Mounia Lalmas (AISTATS, 2021)

Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty. - Judith Bütepage; Lucas Maystre; Mounia Lalmas (ECML/PKDD (2), 2021)

Shifting Consumption towards Diverse Content on Music Streaming Platforms. - Christian Hansen 004; Rishabh Mehrotra; Casper Hansen; Brian Brost; Lucas Maystre; Mounia Lalmas (WSDM, 2021)

Where To Next? A Dynamic Model of User Preferences. - Francesco Sanna Passino; Lucas Maystre; Dmitrii Moor; Ashton Anderson; Mounia Lalmas (WWW, 2021)

The Dynamics of Exploration on Spotify. - Lillio Mok; Samuel F. Way; Lucas Maystre; Ashton Anderson (ICWSM, 2022)

Temporally-Consistent Survival Analysis. - Lucas Maystre; Daniel Russo 001 (NeurIPS, 2022)

Multistate analysis with infinite mixtures of Markov chains. - Lucas Maystre; Tiffany Wu; Roberto Sanchis-Ojeda; Tony Jebara (UAI, 2022)

Using Survival Models to Estimate User Engagement in Online Experiments. - Praveen Chandar; Brian St. Thomas; Lucas Maystre; Vijay Pappu; Roberto Sanchis-Ojeda; Tiffany Wu; Ben Carterette; Mounia Lalmas; Tony Jebara (WWW, 2022)

Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding. - Graham W. Van Goffrier; Lucas Maystre; Ciarán Mark Gilligan-Lee (CLeaR, 2023)

Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. - Thomas M. McDonald 001; Lucas Maystre; Mounia Lalmas; Daniel Russo 001; Kamil Ciosek (KDD, 2023)

Accelerating Creator Audience Building through Centralized Exploration. - Buket Baran; Guilherme Dinis Junior; Antonina Danylenko; Olayinka S. Folorunso; Gösta Forsum; Maksym Lefarov; Lucas Maystre; Yu Zhao (RecSys, 2023)

A Strong Baseline for Batch Imitation Learning. - Matthew Smith; Lucas Maystre; Zhenwen Dai; Kamil Ciosek (CoRR, 2023)

Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective. - Lucas Maystre; Daniel Russo 001; Yu Zhao (CoRR, 2023)

Fast Interactive Search with a Scale-Free Comparison Oracle. - Daniyar Chumbalov; Lars Klein 001; Lucas Maystre; Matthias Grossglauser (CoRR, 2023)

Fast Interactive Search under a Scale-Free Comparison Oracle. - Daniyar Chumbalov; Lars Klein 001; Lucas Maystre; Matthias Grossglauser (UAI, 2024)

On the Importance of Uncertainty in Decision-Making with Large Language Models. - Nicolò Felicioni; Lucas Maystre; Sina Ghiassian; Kamil Ciosek (Trans. Mach. Learn. Res., 2024)

Impatient Bandits: Optimizing for the Long-Term Without Delay. - Kelly W. Zhang; Thomas Baldwin-McDonald; Kamil Ciosek; Lucas Maystre; Daniel Russo 001 (CoRR, 2025)

Incremental Sequence Classification with Temporal Consistency. - Lucas Maystre; Gabriel Barello; Tudor Berariu; Aleix Cambray; Rares Dolga; Alvaro Ortega Gonzalez; Andrei Cristian Nica; David Barber (CoRR, 2025)

Toward Autonomous UI Exploration: The UIExplorer Benchmark. - Andrei Cristian Nica; Akshaya Vishnu Kudlu Shanbhogue; Harshil Shah; Aleix Cambray; Tudor Berariu; Lucas Maystre; David Barber (CoRR, 2025)