Publications by Victor Kristof

Discrete-Choice Mining of Social Processes

Poor decisions and selfish behaviors give rise to seemingly intractable global problems, such as the lack of transparency in democratic processes, the spread of conspiracy theories, and the rise in greenhouse gas emissions. However, people are more predictable than we think, and with machine-learning algorithms and sufficiently large datasets, we can design accurate models of human behavior in a variety of settings. In this thesis, to gain insight into social processes, we develop highly interpretable probabilistic choice-models. We draw from the econometrics literature on discrete-choice models and combine them with matrix factorization methods, Bayesian statistics, and generalized linear models. These predictive models enable interpretability through their learned parameters and latent factors. First, we study the social dynamics behind group collaborations for the collective creation of content, such as in Wikipedia, the Linux kernel, and the European Union law-making process. By combining the Bradley-Terry and Rasch models with matrix factorization and natural language processing, we develop a model of edit acceptance in peer-production systems. We discover controversial components (e.g., Wikipedia articles and European laws) and influential users (e.g., Wikipedia editors and parliamentarians), as well as features that correlate with a high probability of edit acceptance. The latent representations capture non-linear interactions between components and users, and they cluster well into different topics (e.g., historical figures and TV characters in Wikipedia, business and environment in European laws). Second, we develop an algorithm for predicting the outcome of elections and of referenda by combining matrix factorization and generalized linear models. Our algorithm learns representations of votes and regions, which capture ideological and cultural voting patterns (e.g., liberal/conservative, rural/urban), and it predicts the vote results in unobserved regions from partial observations. We test our model on voting data in Germany, Switzerland, and the US, and we deploy it on a Web platform to predict Swiss referendum votes in real-time. On average, our predictions reach a mean absolute error of 1% after observing only 5% of the regions. Third, we study how people perceive the carbon footprint of their day-to-day actions. We cast this problem as a comparison problem between pairs of actions (e.g., the difference between flying across continents and using household appliances), and we develop a statistical model of relative comparisons reminiscent of the Thurstone model in psychometrics. The model learns the usersâ perception as the parameters of a Bayesian linear regression, which enables us to derive an active-learning algorithm to collect data efficiently. Our experiments show that users overestimate the emissions of low-footprint actions and underestimate those of high-footprint actions. Finally, we design a probabilistic model of pairwise-comparison outcomes that capture a wide range of time dynamics. We achieve this by replacing the static parameters of a class of popular pairwise-comparison models with continuous-time Gaussian processes. We also develop an efficient inference algorithm that computes, with only a few linear-time iterations over the data, an approximate Bayesian posterior distribution.

EPFL2021

War of Words II: Enriched Models of Law-Making Processes

Matthias Grossglauser, Victor Kristof, Aswin Suresh, Patrick Thiran

The European Union law-making process is an instance of a peer- production system. We mine a rich dataset of law edits and intro- duce models predicting their adoption by parliamentary committees. Edits are proposed by parliamentarians, and they can be in conflict with edits of other parliamentarians and with the original proposition in the law. Our models combine three different categories of features: (a) Explicit features extracted from data related to the edits, the parliamentarians, and the laws, (b) latent features that capture bi-linear interactions between parliamentarians and laws, and (c) text features of the edits. We show experimentally that this combination enables us to accurately predict the success of the edits. Furthermore, it leads to model parameters that are interpretable, hence provides valuable insight into the law-making process.

ACM2021

Sub-Matrix Factorization for Real-Time Vote Prediction

Matthias Grossglauser, Victor Kristof, Patrick Thiran

We address the problem of predicting aggregate vote outcomes (e.g., national) from partial outcomes (e.g., regional) that are revealed sequentially. We combine matrix factorization techniques and generalized linear models (GLMs) to obtain a flexible, efficient, and accurate algorithm. This algorithm works in two stages: First, it learns representations of the regions from high-dimensional historical data. Second, it uses these representations to fit a GLM to the partially observed results and to predict unobserved results. We show experimentally that our algorithm is able to accurately predict the outcomes of Swiss referenda, U.S. presidential elections, and German legislative elections. We also explore the regional representations in terms of ideological and cultural patterns. Finally, we deploy an online Web platform (www.predikon.ch) to provide real- time vote predictions in Switzerland and a data visualization tool to explore voting behavior. A by-product is a dataset of sequential vote results for 330 referenda and 2196 Swiss municipalities.

2020

War of Words: The Competitive Dynamics of Legislative Processes

Matthias Grossglauser, Victor Kristof, Patrick Thiran

A body of law is an example of a dynamic corpus of text documents that are jointly maintained by a group of editors who compete and collaborate in complex constellations. Our goal is to develop predictive models for this process, thereby shedding light on the competitive dynamics of parliamentarians who make laws. For this purpose, we curated a dataset of 450 000 legislative edits introduced by European parliamentarians over the last ten years. An edit modifies the status quo of a law, and could be in competition with another edit if it modifies the same part of that law. We propose a model for predicting the success of such edits, in the face of both the inertia of the status quo and the competition between overlapping edits. The parameters of this model can be interpreted in terms of the influence of parliamentarians and of the controversy of laws.

ACM / IW3C2 (International World Wide Web Conference Committee)2020

, , , ,

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 the relative carbon footprint of their actions. The formulation of the model enables us to take an active-learning approach to selecting the pairs of actions that are maximally informative about the model parameters. We define a set of 18 actions and collect a dataset of 2183 comparisons from 176 users on a university campus. The early results reveal promising directions to improve climate communication and enhance climate mitigation.

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 replacing the static parameters of a class of popular pairwise-comparison models by continuous-time Gaussian processes; the covariance function of these processes enables expressive dynamics. We develop an efficient inference algorithm that computes an approximate Bayesian posterior distribution. Despite the flexbility of our model, our inference algorithm requires only a few linear-time iterations over the data and can take advantage of modern multiprocessor computer architectures. We apply our model to several historical databases of sports outcomes and find that our approach a) outperforms competing approaches in terms of predictive performance, b) scales to millions of observations, and c) generates compelling visualizations that help in understanding and interpreting the data.

ASSOC COMPUTING MACHINERY2019

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 or consist of a highly specialized predictor that is tailored to a specific peer-production system. In this work, we explore a different point in the solution space that goes beyond user reputation but does not involve any content-based feature of the edits. We view each edit as a game between the editor and the component of the project. We posit that the probability that an edit is accepted is a function of the editor's skill, of the difficulty of editing the component and of a user-component interaction term. Our model is broadly applicable, as it only requires observing data about who makes an edit, what the edit affects and whether the edit survives or not. We apply our model on Wikipedia and the Linux kernel, two examples of large-scale peer-production systems, and we seek to understand whether it can effectively predict edit survival: in both cases, we provide a positive answer. Our approach significantly outperforms those based solely on user reputation and bridges the gap with specialized predictors that use content-based features. It is simple to implement, computationally inexpensive, and in addition it enables us to discover interesting structure in the data.

ACM2018

, ,

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 through kernel functions. Using this connection, we tackle the problem of predicting outcomes of football matches between national teams. We develop a player kernel that relates any two football matches through the players lined up on the field. This makes it possible to share knowledge gained from observing matches between clubs (available in large quantities) and matches between national teams (available only in limited quantities). We evaluate our approach on the Euro 2008, 2012 and 2016 final tournaments.

2016