Publications by Lucas Maystre | EPFL Graph Search

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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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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. This conveys insight into several recently proposed spectral inf ...

Lucas Maystre

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Fast Interactive Search under a Scale-Free Comparison Oracle

Efficient Learning from Comparisons

Collaborative Recurrent Neural Networks for Dynamic Recommender Systems

ChoiceRank: Identifying Preferences from Node Traffic in Networks

Scalable and Efficient Comparison-based Search without Features

Pairwise Comparisons with Flexible Time-Dynamics

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

Can Who-Edits-What Predict Edit Survival?

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

Fast and Accurate Inference of Plackett-Luce Models

Mitigating Epidemics through Mobile Micro-measures