Publications de Lucas Maystre | EPFL Graph Search

Fast Interactive Search under a Scale-Free Comparison Oracle

Matthias Grossglauser, Lucas Maystre, Daniyar Chumbalov

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?" Instead of formulating an explicit query (such as one or several keywords), the user navigates tow ...

2024

Scalable and Efficient Comparison-based Search without Features

Matthias Grossglauser, Lucas Maystre, Daniyar Chumbalov

We consider the problem of finding a target object t using pairwise comparisons, by asking an oracle questions of the form “Which object from the pair (i, j) is more similar to t?”. Objects live in a space of latent features, from which the oracle generate ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2020

A User Study of Perceived Carbon Footprint

Patrick Thiran, Matthias Grossglauser, Victor Kristof, Robin Adrien Zbinden, Lucas Maystre, Valentin Quelquejay-Leclère

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 design a system to probe people's perception from simple pairwise comparisons of ...

2019

Pairwise Comparisons with Flexible Time-Dynamics

Matthias Grossglauser, Victor Kristof, Lucas Maystre

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. We achieve this by replac ...

ASSOC COMPUTING MACHINERY2019

Efficient Learning from Comparisons

Lucas Maystre

Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...

EPFL2018

Can Who-Edits-What Predict Edit Survival?

Matthias Grossglauser, Victor Kristof, Lucas Maystre

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. Existing solutions either rely on a user reputation system o ...

ACM2018

Just Sort It! A Simple and Effective Approach to Active Preference Learning

Matthias Grossglauser, Lucas Maystre

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. If all comparison outcomes are consistent with the ranking, the optimal sol ...

2017

ChoiceRank: Identifying Preferences from Node Traffic in Networks

Matthias Grossglauser, Lucas Maystre

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 task of learning edge transition probabilities. We cast it as a preference lear ...

2017

The Player Kernel: Learning Team Strengths Based on Implicit Player Contributions

Matthias Grossglauser, Victor Kristof, Lucas Maystre

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) a principled way of dealing with uncertainty and b) rich models, specified t ...

2016

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

Matthias Grossglauser, Lucas Maystre, Young Jun Ko

Modern technologies enable us to record sequences of online user activity at an unprecedented scale. Although such activity logs are abundantly available, most approaches to recommender systems are based on the rating-prediction paradigm, ignoring temporal ...

2016