30 citations · 69 across the 12 of their papers we have counts for
13 papers
Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay
Thomas M. McDonald, Lucas Maystre, Mounia Lalmas +2
Recommender systems are a ubiquitous feature of online platforms. Increasingly, they are explicitly tasked with increasing users' long-term satisfaction. In this context, we study…
Fast Interactive Search with a Scale-Free Comparison Oracle
Daniyar Chumbalov, Lars Klein, Lucas Maystre +1
A comparison-based search algorithm lets a user find a target item in a database by answering queries of the form, ``Which of items and is closer to ?'' Instead of f…
Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding
Graham Van Goffrier, Lucas Maystre, Ciarán Gilligan-Lee
Understanding and quantifying cause and effect is an important problem in many domains. The generally-agreed solution to this problem is to perform a randomised controlled trial. H…
A Strong Baseline for Batch Imitation Learning
Matthew Smith, Lucas Maystre, Zhenwen Dai +1
Imitation of expert behaviour is a highly desirable and safe approach to the problem of sequential decision making. We provide an easy-to-implement, novel algorithm for imitation l…
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective
Lucas Maystre, Daniel Russo, Yu Zhao
We present a novel podcast recommender system deployed at industrial scale. This system successfully optimizes personal listening journeys that unfold over months for hundreds of m…
A User Study of Perceived Carbon Footprint
Victor Kristof, Valentin Quelquejay-Leclère, Robin Zbinden +3
We propose a statistical model to understand people's perception of their carbon footprint. Driven by the observation that few people think of CO2 impact in absolute terms, we desi…
Scalable and Efficient Comparison-based Search without Features
Daniyar Chumbalov, Lucas Maystre, Matthias Grossglauser
We consider the problem of finding a target object using pairwise comparisons, by asking an oracle questions of the form \emph{"Which object from the pair is more simil…
Pairwise Comparisons with Flexible Time-Dynamics
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 outcom…
Can Who-Edits-What Predict Edit Survival?
Ali Batuhan Yardım, Victor Kristof, Lucas Maystre +1
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…
ChoiceRank: Identifying Preferences from Node Traffic in Networks
Lucas Maystre, Matthias Grossglauser
Understanding how users navigate in a network is of high interest in many applications. We consider a setting where only aggregate node-level traffic is observed and tackle the tas…
The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions
Lucas Maystre, Victor Kristof, Antonio J. González Ferrer +1
In this work, we draw attention to a connection between skill-based models of game outcomes and Gaussian process classification models. The Gaussian process perspective enables a)…
Just Sort It! A Simple and Effective Approach to Active Preference Learning
Lucas Maystre, Matthias Grossglauser
We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly.…
Mitigating Epidemics through Mobile Micro-measures
Mohamed Kafsi, Ehsan Kazemi, Lucas Maystre +3
Epidemics of infectious diseases are among the largest threats to the quality of life and the economic and social well-being of developing countries. The arsenal of measures agains…