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Lucas Maystre
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2020 – today
- 2024
- [j1]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) - [i14]Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek:
On the Importance of Uncertainty in Decision-Making with Large Language Models. CoRR abs/2404.02649 (2024) - 2023
- [c21]Graham W. 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: 791-813 - [c20]Thomas M. McDonald
, Lucas Maystre
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. KDD 2023: 1687-1697 - [c19]Buket Baran
Accelerating Creator Audience Building through Centralized Exploration. RecSys 2023: 70-73 - [i13]Matthew Smith, Lucas Maystre, Zhenwen Dai, Kamil Ciosek:
A Strong Baseline for Batch Imitation Learning. CoRR abs/2302.02788 (2023) - [i12]Lucas Maystre, Daniel Russo, Yu Zhao:
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective. CoRR abs/2302.03561 (2023) - [i11]Graham W. 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 abs/2302.10625 (2023) - [i10]Daniyar Chumbalov, Lars Klein, Lucas Maystre, Matthias Grossglauser:
Fast Interactive Search with a Scale-Free Comparison Oracle. CoRR abs/2306.01814 (2023) - [i9]Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek:
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. CoRR abs/2307.09943 (2023) - 2022
- [c18]Lillio Mok, Samuel F. Way, Lucas Maystre, Ashton Anderson:
The Dynamics of Exploration on Spotify. ICWSM 2022: 663-674 - [c17]Lucas Maystre, Daniel Russo:
Temporally-Consistent Survival Analysis. NeurIPS 2022 - [c16]Lucas Maystre, Tiffany Wu, Roberto Sanchis-Ojeda, Tony Jebara:
Multistate analysis with infinite mixtures of Markov chains. UAI 2022: 1350-1359 - [c15]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: 3186-3195 - 2021
- [c14]Lucas Maystre, Nagarjuna Kumarappan, Judith Bütepage, Mounia Lalmas:
Collaborative Classification from Noisy Labels. AISTATS 2021: 1639-1647 - [c13]Judith Bütepage
Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty. ECML/PKDD (2) 2021: 84-99 - [c12]Christian Hansen, Rishabh Mehrotra, Casper Hansen, Brian Brost, Lucas Maystre, Mounia Lalmas:
Shifting Consumption towards Diverse Content on Music Streaming Platforms. WSDM 2021: 238-246 - [c11]Francesco Sanna Passino, Lucas Maystre, Dmitrii Moor, Ashton Anderson, Mounia Lalmas:
Where To Next? A Dynamic Model of User Preferences. WWW 2021: 3210-3220 - 2020
- [c10]Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser:
Scalable and Efficient Comparison-based Search without Features. ICML 2020: 1995-2005 - [c9]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: 53-62 - [c8]Ashton Anderson, Lucas Maystre, Ian Anderson, Rishabh Mehrotra, Mounia Lalmas:
Algorithmic Effects on the Diversity of Consumption on Spotify. WWW 2020: 2155-2165
2010 – 2019
- 2019
- [c7]Lucas Maystre, Victor Kristof, Matthias Grossglauser:
Pairwise Comparisons with Flexible Time-Dynamics. KDD 2019: 1236-1246 - [i8]Lucas Maystre, Victor Kristof, Matthias Grossglauser:
Linear-Time Inference for Pairwise Comparisons with Gaussian-Process Dynamics. CoRR abs/1903.07746 (2019) - [i7]Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser:
Learning to Search Efficiently Using Comparisons. CoRR abs/1905.05049 (2019) - [i6]Victor Kristof, Valentin Quelquejay-Leclère, Robin Zbinden, Lucas Maystre, Matthias Grossglauser, Patrick Thiran:
A User Study of Perceived Carbon Footprint. CoRR abs/1911.11658 (2019) - 2018
- [b1]Lucas Maystre:
Efficient Learning from Comparisons. EPFL, Switzerland, 2018 - [c6]Ali Batuhan Yardim, Victor Kristof, Lucas Maystre, Matthias Grossglauser:
Can Who-Edits-What Predict Edit Survival? KDD 2018: 2604-2613 - [p1]Lucas Maystre:
Efficient Learning from Comparisons. Ausgezeichnete Informatikdissertationen 2018: 151-160 - [i5]Ali Batuhan Yardim, Victor Kristof, Lucas Maystre, Matthias Grossglauser:
Can Who-Edits-What Predict Edit Survival? CoRR abs/1801.04159 (2018) - 2017
- [c5]Lucas Maystre, Matthias Grossglauser:
Just Sort It! A Simple and Effective Approach to Active Preference Learning. ICML 2017: 2344-2353 - [c4]Lucas Maystre, Matthias Grossglauser:
ChoiceRank: Identifying Preferences from Node Traffic in Networks. ICML 2017: 2354-2362 - 2016
- [c3]Young-Jun Ko, Lucas Maystre, Matthias Grossglauser:
Collaborative Recurrent Neural Networks for Dynamic Recommender Systems. ACML 2016: 366-381 - [c2]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 - [i4]Lucas Maystre, Victor Kristof, Antonio J. González Ferrer, Matthias Grossglauser:
The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions. CoRR abs/1609.01176 (2016) - [i3]Lucas Maystre, Matthias Grossglauser:
ChoiceRank: Identifying Preferences from Node Traffic in Networks. CoRR abs/1610.06525 (2016) - 2015
- [c1]Lucas Maystre, Matthias Grossglauser:
Fast and Accurate Inference of Plackett-Luce Models. NIPS 2015: 172-180 - [i2]Lucas Maystre, Matthias Grossglauser:
Robust Active Ranking from Sparse Noisy Comparisons. CoRR abs/1502.05556 (2015) - 2013
- [i1]Mohamed Kafsi, Ehsan Kazemi, Lucas Maystre, Lyudmila Yartseva, Matthias Grossglauser, Patrick Thiran:
Mitigating Epidemics through Mobile Micro-measures. CoRR abs/1307.2084 (2013)
Coauthor Index
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last updated on 2024-08-10 00:32 CEST by the dblp team
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