no code implementations • 14 Jan 2025 • Kelly W. Zhang, Thomas Baldwin-McDonald, Kamil Ciosek, Lucas Maystre, Daniel Russo
Second, we devise a bandit algorithm that quickly learns to identify content aligned with long-term success using this new predictive model.
no code implementations • 3 Apr 2024 • Nicolò Felicioni, Lucas Maystre, Sina Ghiassian, Kamil Ciosek
We compare this baseline to LLM bandits that make active use of uncertainty estimation by integrating the uncertainty in a Thompson Sampling policy.
no code implementations • 27 Feb 2024 • Rares Dolga, Lucas Maystre, Marius Cobzarenco, David Barber
The time complexity of the standard attention mechanism in transformers scales quadratically with sequence length.
1 code implementation • 19 Jul 2023 • Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek
In this context, we study a content exploration task, which we formalize as a multi-armed bandit problem with delayed rewards.
no code implementations • 2 Jun 2023 • Daniyar Chumbalov, Lars Klein, Lucas Maystre, Matthias Grossglauser
A comparison-based search algorithm lets a user find a target item $t$ in a database by answering queries of the form, ``Which of items $i$ and $j$ is closer to $t$?''
no code implementations • 21 Feb 2023 • Graham Van Goffrier, Lucas Maystre, Ciarán Gilligan-Lee
In this paper, we study the identification and estimation of long-term treatment effects when both experimental and observational data are available.
no code implementations • 7 Feb 2023 • Lucas Maystre, Daniel Russo, Yu Zhao
Then, within this model, we identify our approach as a policy improvement update to a component of the existing recommender system, enhanced by tailored modeling of value functions and user-state representations.
no code implementations • 6 Feb 2023 • Matthew Smith, Lucas Maystre, Zhenwen Dai, Kamil Ciosek
Imitation of expert behaviour is a highly desirable and safe approach to the problem of sequential decision making.
no code implementations • 26 Nov 2019 • Victor Kristof, Valentin Quelquejay-Leclère, Robin Zbinden, Lucas Maystre, Matthias Grossglauser, Patrick Thiran
We propose a statistical model to understand people's perception of their carbon footprint.
no code implementations • ICML 2020 • Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser
We consider the problem of finding a target object $t$ using pairwise comparisons, by asking an oracle questions of the form \emph{"Which object from the pair $(i, j)$ is more similar to $t$?"}.
2 code implementations • 18 Mar 2019 • Lucas Maystre, Victor Kristof, Matthias Grossglauser
Inspired by applications in sports where the skill of players or teams competing against each other varies over time, we propose a probabilistic model of pairwise-comparison outcomes that can capture a wide range of time dynamics.
1 code implementation • 12 Jan 2018 • Ali Batuhan Yardım, Victor Kristof, Lucas Maystre, Matthias Grossglauser
As the number of contributors to online peer-production systems grows, it becomes increasingly important to predict whether the edits that users make will eventually be beneficial to the project.
no code implementations • ICML 2017 • Lucas Maystre, Matthias Grossglauser
We consider a setting where only aggregate node-level traffic is observed and tackle the task of learning edge transition probabilities.
no code implementations • 5 Sep 2016 • Lucas Maystre, Victor Kristof, Antonio J. González Ferrer, Matthias Grossglauser
In this work, we draw attention to a connection between skill-based models of game outcomes and Gaussian process classification models.
no code implementations • NeurIPS 2015 • Lucas Maystre, Matthias Grossglauser
We show that the maximum-likelihood (ML) estimate of models derived from Luce's choice axiom (e. g., the Plackett-Luce model) can be expressed as the stationary distribution of a Markov chain.
no code implementations • ICML 2017 • Lucas Maystre, Matthias Grossglauser
We address the problem of learning a ranking by using adaptively chosen pairwise comparisons.