activity
20162023
most citedThe Player Kernel: Learning Team Strengths Based on Implicit Player Contributions

4 citations · 7 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2023

Studying Lobby Influence in the European Parliament

Aswin Suresh, Lazar Radojevic, Francesco Salvi +3

We present a method based on natural language processing (NLP), for studying the influence of interest groups (lobbies) in the law-making process in the European Parliament (EP). W…

stat.ML2019★ 1 cited

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…

stat.ML2019

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…

stat.AP2018★ 2 cited

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…

cs.LG2016★ 4 cited

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)…