FAIRPLAY

FAIRPLAY - 2025

2025‌Activity reportProject-TeamFAIRPLAY‌‌

RNSR: 202224251U

Research center Inria Saclay Centre at‌ Institut Polytechnique de Paris‌
In partnership with:Institut‌‌ Polytechnique de Paris, Criteo
Team name: Coopetitive AI:‌ Fairness, Privacy, Incentives
In‌ collaboration with:Centre de‌‌ Recherche en Economie et Stastistique

Creation of the‌ Project-Team: 2022 March 01‌

Each year, Inria research‌‌ teams publish an Activity Report presenting their work‌ and results over the‌ reporting period. These reports‌‌ follow a common structure, with some optional sections‌ depending on the specific‌ team. They typically begin‌‌ by outlining the overall objectives and research programme,‌ including the main research‌ themes, goals, and methodological‌‌ approaches. They also describe the application domains targeted‌ by the team, highlighting‌ the scientific or societal‌‌ contexts in which their work is situated.

The‌ reports then present the‌ highlights of the year,‌‌ covering major scientific achievements, software developments, or teaching‌ contributions. When relevant, they‌ include sections on software,‌‌ platforms, and open data, detailing the tools developed‌ and how they are‌ shared. A substantial part‌‌ is dedicated to new results, where scientific contributions‌ are described in detail,‌ often with subsections specifying‌‌ participants and associated keywords.‌

Finally, the Activity Report addresses funding, contracts, partnerships,‌ and collaborations at various levels, from industrial agreements‌ to international cooperations. It also covers dissemination and‌ teaching activities, such as participation in scientific events,‌ outreach, and supervision. The document concludes with a‌ presentation of scientific production, including major publications and‌ those produced during the year.

Keywords

Computer Science‌ and Digital Science

A4.8. Privacy-enhancing technologies
A8.11. Game‌ Theory
A9.2. Machine learning
A9.9. Distributed AI, Multi-agent‌

1 Team members, visitors, external collaborators‌

Research Scientists

Patrick Loiseau [Team leader,‌ INRIA, Senior Researcher, HDR]
Marc‌ Abeille [CRITEO, Industrial member]
Benjamin‌ Heymann [CRITEO, Industrial member]
Simon‌ Mauras [INRIA, Researcher]
Hugo Richard‌ [CRITEO, Industrial member]
Mariia Vladimirova‌ [CRITEO, Industrial member]
Maxime Vono‌ [CRITEO, Industrial member]

Faculty Members‌

Matthieu Lerasle [ENSAE, Professor]
Vianney‌ Perchet [CRITEO, Professor, HDR]‌
Cristina Rio Butucea [GENES, Professor]‌

Post-Doctoral Fellows

Achraf Azize [ENSAE]
Lorenzo‌ Croissant [ENSAE]
Simon Finster [INRIA‌, Post-Doctoral Fellow, until Sep 2025]‌
Junghun Kim [ENSAE]
Denis Sokolov [‌INRIA, Post-Doctoral Fellow, until Nov 2025‌]
Bartholome Vieille [INRIA, Post-Doctoral Fellow‌]

PhD Students

Louise Allain [INRIA,‌ from Oct 2025]
Ahmed Ben Yahmed [‌CRITEO, CIFRE]
Ziyad Benomar [ENSAE‌, until Sep 2025]
Maria Cherifa [‌CRITEO, CIFRE]
Hafedh El Ferchichi [‌ENSAE]
Come Fiegel [ENSAE]
Reda‌ Jalal [INRIA]
Mike Liu [ENSAE‌]
Mathieu Molina [INRIA, until Sep‌ 2025]
Giovanni Montanari [INRIA]
Corentin‌ Pla [CRITEO, CIFRE]
Marius Potfer‌ [EDF, CIFRE]
Naila Sebastián Esandi‌ [ENSAE, from Oct 2025]
Mélissa‌ Tamine [CRITEO, CIFRE]
Matilde Tullii‌ [ENSAE]
Remi Verroneau [FOOTOVISION,‌ CIFRE]
Axel Xerri [INRIA]
Minrui‌ Xu [ENSAE]

Technical Staff

Eugenie Patard‌ [INRIA, Engineer, from Nov 2025‌]

Interns and Apprentices

Naila Carmen Sebastian Esandi‌ [INRIA, Intern, from May 2025‌ until Sep 2025]

Administrative Assistant

Melanie Da‌ Silva [INRIA]

External Collaborator

Clément Calauzenes‌ [CRITEO]

2 Overall objectives

2.1 Broad‌ context

One of the principal objectives of Machine‌ Learning (ML) is to automatically discover using past‌ data some underlying structure behind a data generating‌ process in order either to explain past observations‌ or, perhaps more importantly, to make predictions and/or‌ to optimize decisions made on future instances. The‌ area of ML has exploded over the past‌ decade and has had a tremendous impact in‌ many application domains such as computer vision or‌ bioinformatics.

Most of the current ML literature focuses on the case of‌ a single agent (an‌ algorithm) trying to complete‌‌ some learning task based on gathered data that‌ follows an exogenous distribution‌ independent of the algorithm.‌‌ One of the key assumptions is that this‌ data has sufficient “regularity”‌ for classical techniques to‌‌ work. This classical paradigm of “a single agent‌ learning on nice data”,‌ however, is no longer‌‌ adequate for many practical and crucial tasks that‌ imply users (who own‌ the gathered data) and/or‌‌ other (learning) agents that are also trying to‌ optimize their own objectives‌ simultaneously, in a competitive‌‌ or conflicting way. This is the case, for‌ instance, in most learning‌ tasks related to Internet‌‌ applications (content recommendation/ranking, ad auctions, fraud detection, etc.).‌ Moreover, as such learning‌ tasks rely on users'‌‌ personal data and as their outcome affect users‌ in return, it is‌ no longer sufficient to‌‌ focus on optimizing prediction performance metrics—it becomes crucial‌ to consider societal and‌ ethical aspects such as‌‌ fairness or privacy.

The field of single agent‌ ML builds on techniques‌ from domains such as‌‌ statistics, optimization, or functional analysis. When different agents‌ are involved, a strategic‌ aspect inherent in game‌‌ theory enters the picture. Indeed, interactions—either positive or‌ negative—between rational entities (firms,‌ single user at home,‌‌ algorithms, etc.) foster individual strategic behavior such as‌ hiding information, misleading other‌ agents, free-riding, etc. Unfortunately,‌‌ this selfishness degrades the quality of the data‌ or of the predictions,‌ prevents efficient learning and‌‌ overall may diminish the social welfare. These strategic‌ aspects, together with the‌ decentralized nature of decision‌‌ making in a multi-agent environment, also make it‌ harder to build algorithms‌ that meet fairness and‌‌ privacy constraints.

The overarching objective of FAIRPLAY is‌ to create algorithms that‌ learn for and with‌‌ users—and techniques to analyze them—, that is‌ to create procedures able‌ to perform classical learning‌‌ tasks (prediction, decision, explanation) when the data is‌ generated or provided by‌ strategic agents, possibly in‌‌ the presence of other competing learning agents, while‌ respecting the fairness and‌ privacy of the involved‌‌ users. To that end, we will naturally rely‌ on multi-agent models where‌ the different agents may‌‌ be either agents generating or providing data, or‌ agents learning in a‌ way that interacts with‌‌ other agents; and we will put a special‌ focus on societal and‌ ethical aspects, in particular‌‌ fairness and privacy. Note that in FAIRPLAY, we‌ focus on the technical‌ challenges inherent to formalizing‌‌ mathematically and respecting ethical properties such as non-discrimination‌ or privacy, often seen‌ as constraints in the‌‌ learning procedure. Nevertheless, throughout the team's life, we‌ will reflect on these‌ mathematical definitions for the‌‌ particular applications studied, in particular their philosophical roots‌ and legal interpretation, through‌ interactions with HSS researchers‌‌ and with legal specialists (from Criteo).

2.1.1 Multi-agent‌ systems

Any company developing‌ and implementing ML algorithms‌‌ is in fact one agent within a large‌ network of users and‌ other firms. Assuming that‌‌ the data is i.i.d.‌ and can be treated irrespectively of the environment‌ response—as is done in the classical ML paradigm—might‌ be a good first approximation, but should be‌ overcome. Users, clients, suppliers, and competitors are adaptive‌ and change their behavior depending on each other's‌ interactions. The future of many ML companies—such as‌ Criteo—will consist in creating platforms matching the demand‌ (created by their users) to the offer (proposed‌ by their clients), under the system constraints (imposed‌ by suppliers and competitors). Each of these agents‌ have different, conflicting interests that should be taken‌ into account in the model, which naturally becomes‌ a multi-agent model.

Each agent in a multi-agent‌ system may be modeled as having their own‌ utility function $u_{i}$ that can depend on‌ the action of other agents. Then, there are‌ two main types of objectives: individual or collective‌ 104. If each agent is making their‌ own decision, then they can be modeled as‌ each optimizing their own individual utility (which may‌ include personal benefit as well as other considerations‌ such as altruism where appropriate) unilaterally and in‌ a decentralized way. This is why a mechanism‌ providing correct incentives to agents is often necessary.‌ At the other extreme, social welfare is the‌ collective objective defined as the cumulative sum of‌ utilities of all agents. To optimize it, it‌ is almost always necessary to consider a centralized‌ optimization or learning protocol. A key question in‌ multi-agent systems is to apprehend the “social cost”‌ of letting agents optimize their own utility by‌ choosing unilaterally their decision compared to the one‌ maximizing social welfare; this is often measured by‌ the “price of anarchy”/“price of stability” 113:‌ the ratio of the maximum social welfare to‌ the (worst/best) social welfare when agents optimize individually.‌

The natural language to model and study multi-agent‌ systems is game theory—see below for a‌ list of tools and techniques on which FAIRPLAY‌ relies, game theory being the first of them.‌ Multi-agent systems have been studied in the past;‌ but not with a focus on learning systems‌ where agents are either learning or providing data,‌ which is our focus in FAIRPLAY and leads‌ to a blend of game theory and learning‌ techniques. We note here again that, wherever appropriate,‌ we shall reflect (in part together with colleagues‌ from HSS) on the soundness of the utility‌ framework for the considered applications.

2.1.2 Societal aspects‌ and ethics

There are several important ethical aspects‌ that must be investigated in multi-agent systems involving‌ users either as data providers or as individuals‌ affected by the ML agent decision (or both).‌

Fairness and Discrimination

When ML decisions directly affect‌ humans, it is important to ensure that they‌ do not violate fairness principles, be they based‌ on ethical or legal grounds. As ML made‌ its way in many areas of decision making,‌ it was unfortunately repeatedly observed that it can‌ lead to discrimination (regardless of whether or not it is intentional) based‌ on gender, race, age,‌ or other sensitive attributes.‌‌ This was observed in online targeted advertisement 98‌, 124, 38‌, 82, 98‌‌, 40, but also in many other‌ applications such as hiring‌ 69, data-driven healthcare‌‌ 76, or justice 99. Biases also‌ have the unfortunate tendency‌ to reinforce. An operating‌‌ multi-agent learning system should be able in the‌ long run to get‌ rid by itself of‌‌ inherent population biases, that is, be fair amongst‌ users irrespective of the‌ improperly constructed dataset.

The‌‌ mathematical formulation of fairness has been debated in‌ recent works. Although a‌ few initial works proposed‌‌ a notion of individual fairness, which mandates‌ that “similar individuals” receive‌ “similar outcomes” 71,‌‌ this notion was quickly found unpractical because it‌ relies on a metric‌ to define closeness that‌‌ makes the definition somewhat arbitrary. Most of the‌ works then focused on‌ notions of group fairness‌‌, which mandate equality of outcome “on average”‌ across different groups defined‌ by sensitive attributes (e.g.,‌‌ race, gender, religious belief, etc.). Most of the‌ works on group fairness‌ focus on the classification‌‌ problem (e.g., classifying whether a job applicant is‌ good or not for‌ the job) where each‌‌ data example $({X}_{i}, Y_{i‌})$ contains a set‌ of features $X_{i‌‌}$ and a true label $Y_{i} \in {‌ 0, 1}‌$ and the goal is‌‌ to make a prediction ${\hat{Y}}_{i}$ based‌ on the features ${X‌}_{i}$ that has a‌‌ high probability to be equal to the true‌ label. Assuming that there‌ is a single sensitive‌‌ attribute $s_{i}$ that can take two values‌ $a$ or $b$ ,‌ this defines two groups:‌‌ those for whom ${s}_{i} = a$ and‌ those for whom ${s‌}_{i} = b$ .‌‌ There are several different concepts of group fairness‌ that can be considered;‌ we shall especially focus‌‌ on demographic parity (DP), which prescribes $P (‌ {\hat{Y}}_{i} =‌ 1 | s_{i‌‌} = a) = P ({\overset{^‌}{Y}}_{i} = 1 |‌ s_{i} = b‌‌)$ and equal opportunity (EO) 83, which‌ mandates that $P (‌ {\hat{Y}}_{i} =‌‌ 1 | s_{i} = a, {Y‌}_{i} = 1)‌ = P ({\hat{Y‌‌}}_{i} = 1 | s_{i} =‌ b, Y_{i‌} = 1)$ .‌‌

The fair classification literature proposed, for each of‌ these fairness notions, ways‌ to train fair classifiers‌‌ based on three main ideas: pre-processing 132,‌ in-processing 130, 131‌, 127, and‌‌ post-processing 83. All of these works, however,‌ focus on idealized situations‌ where a single decision-maker‌‌ has access to ground truth data with the‌ sensitive features and labels‌ in order to train‌‌ classifiers that respect fairness constraints. We use similar‌ group fairness definitions and‌ extend them (in particular‌‌ through causality), but our‌ goal is to go further in terms of‌ algorithms by modeling practical scenarios with multiple decision-makers‌ and incomplete information (in particular lack of ground‌ truth on the labels).

Privacy vs. Incentives

ML‌ algorithms, in particular in Internet applications, often rely‌ on users' personal information (whether it is directly‌ their personal data or indirectly some hidden “type”‌ – gender, ethnicity, behaviors, etc.). Nevertheless, users may‌ be willing to provide their personal information if‌ it increases their utility. This brings a number‌ of key questions. First, how can we learn‌ while protecting users' privacy (and how should privacy‌ even be defined)? Second, finding the right balance‌ between those two a-priori incompatible concepts is challenging;‌ how much (and even simply how) should an‌ agent be compensated for providing useful and accurate‌ data?

Differential privacy is the most widely used‌ private learning framework 70, 72, 119‌ and ensures that the output of an algorithm‌ does not significantly depend on a single element‌ of the whole dataset. These privacy constraints are‌ often too strong for economic applications (as illustrated‌ before, it is sometimes optimal to disclose some‌ private information). $f$ -divergence privacy costs have thus‌ been proposed in recent literature as a promising‌ alternative 62. These $f$ -divergences, such as‌ Kullback-Leibler, are also used by economists to measure‌ the cost of information from a Bayesian perspective,‌ as in the rational inattention literature 123,‌ 106, 101. It was only recently‌ that this approach was considered to measure “privacy‌ losses” in economic mechanisms 73. In this‌ model, the mechanism designer has some prior belief‌ on the unobserved and private information. After observing‌ the player's action, this belief is updated and‌ the cost of information corresponds to the KL‌ between the prior and posterior distributions of this‌ private information.

This privacy concept can be refined‌ up to a single user level, into the‌ so-called local differential privacy. Informally speaking, the algorithm‌ output can also depend on a single user‌ data that still must be kept private. Estimation‌ are actually sometimes more challenging under this constraint,‌ i.e., estimation rates degrade 120, 57,‌ 58 but is sometimes more adapted to handle‌ user-generated data 78.

Interestingly, we note that‌ the notions of privacy and fairness are somewhat‌ incompatible. This will motivate Theme 2 developed in‌ our research program.

2.2 A large variety of‌ tools and techniques

Analyzing multi-agent learning systems with‌ ethical constraints will require us to use, develop,‌ and merge several different theoretical tools and techniques.‌ We describe the main ones here. Note that‌ although FAIRPLAY is motivated by practical use-cases and‌ applications, part of the team's objectives is to‌ improve those tools as necessary to tackle the‌ problems studied.

Game theory and economics

Game theory‌ 77 is the natural mathematical tool to model‌ multiple interacting decision-makers (called players). A game is‌ defined by a set of players, a set of possible actions for‌ each player, and a‌ payoff function for each‌‌ player that can depend on the actions of‌ all the players (that‌ is the distinguishing feature‌‌ of a game compared to an optimization problem).‌ The most standard solution‌ concept is the so-called‌‌ Nash equilibrium, which is defined as a strategy‌ profile (i.e., a collection‌ of possibly randomized action‌‌ for each player) such that each player is‌ at best response (i.e.,‌ has the maximum payoff‌‌ given the others' strategies). It is a “static”‌ (one-shot) solution concept, but‌ there also exist dynamic‌‌ solution concepts for repeated games 61, 108‌.

Online and reinforcement‌ learning 54

In online‌‌ learning (a.k.a. multi-armed bandit 55, 115),‌ data is gathered and‌ treated on the fly.‌‌ For instance, consider an online binary classification problem.‌ Some unlabelled data ${X‌}_{t} \in R^{d‌‌}$ is observed, and the agent predicts its label‌ $Y_{t}$ ; let‌ us denote ${\overset{^‌‌}{Y}}_{t} \in \pm 1$ the prediction. The agent‌ potentially observes the loss‌ $1 {Y_{t‌‌} \neq {\hat{Y}}_{t}}$ and then receives‌ another new unlabeled data‌ example $X_{t +‌‌ 1}$ . In that specific problem, the typical‌ learning objective is to‌ perform asymptotically as good‌‌ as the best classifier $f^{*}$ in some‌ given class $ℱ$ ,‌ i.e., such that the‌‌ loss $\sum_{t = 1}^{T} 1 {‌ Y_{t} \neq {\hat{Y‌}}_{t}}$ is‌‌ $o (T)$ -close to ${max}_{f‌ \in ℱ} \sum_{t‌ = 1}^{T} 1‌‌ {Y_{t} \neq f (X_{t‌})}$ ; the‌ difference between those terms‌‌ is called regret. The more general model‌ with an underlying state‌ of the world ${S‌‌}_{t} \in 𝒮$ that evolves at each step‌ following some Markov Decision‌ Process (MDP, i.e., the‌‌ transition matrix from ${S}_{t}$ to $S_{t‌ + 1}$ depend on‌ the actions of the‌‌ agent) and impacts the loss function is called‌ reinforcement learning (RL). RL‌ is an incredibly powerful‌‌ learning technique, provided enough data are available since‌ learning is usually quite‌ slow. This is why‌‌ the recent successes involve settings with heavy simulations‌ (like games) or well-understood‌ physical systems (like robots).‌‌

These techniques will be central to our approach‌ as we aim to‌ model problems where ground‌‌ truth data is not available upfront and problems‌ involving sequential decision making.‌ There have been some‌‌ successful first results in that direction. For instance,‌ there are applications (e.g.,‌ cognitive radio) where several‌‌ agents (users) aim at finding a matching with‌ resources (the different bandwidth).‌ They can do that‌‌ by “probing” the resources, estimating their preferences and‌ trying to find some‌ stable matchings 52,‌‌ 100.

Online algorithms 50 and theoretical computer‌ science

Online algorithms are‌ closely related to online‌‌ learning with a major twist. In online learning,‌ the agent has “0-look‌ ahead”; for instance, in‌‌ the online binary classification‌ example, the loss at stage $t$ was $1‌ {Y_{t} \neq {\hat{Y}}_{t}}‌$ but $Y_{t}$ was not known in advance.‌ The comparison class, on the other hand, was‌ the empirical performance of a given set of‌ classifiers. In online algorithms, the agents have “1-look‌ ahead”; in the classification example, this means that‌ $Y_{t}$ is known before choosing ${\overset{^‌}{Y}}_{t}$ . But the overall objective is obviously‌ no longer the minimisation of the empirical error,‌ but the minimisation of this error plus the‌ total number of changes (say). The comparison class‌ is then larger, namely a subset of admissible‌ (or the whole set) sequences of prediction ${‌ \pm 1}^{T}$ . The typical and‌ relevant example of online problem relevant for Criteo‌ that will be investigated is the matching problem:‌ agents and resources arrive sequentially and must be,‌ if possible, paired together as fast as possible‌ (and as successfully as possible). Variants of these‌ problems include the optimal stopping time question (when/how‌ make a final decision) such as prophet inequalities‌ and related questions 67,

Optimal transport 125‌

Optimal transport is a quite old problem introduced‌ by Monge where an agent aims at moving‌ a pile of sand to fill a hole‌ at the smallest possible price. Formally speaking, given‌ two probability measures $μ$ and $ν$ on some‌ space $𝒳$ , the optimal transport problem consist‌ in finding (if it exists, otherwise the problem‌ can be relaxed) a transport map $T :‌ 𝒳 \to 𝒳$ that minimizes $\int_{𝒳} c‌ (x, T (x))‌ d μ (x)$ for some cost‌ function $c : {𝒳}^{2} \to R$ ,‌ under the constraint that $T ♯ μ =‌ ν$ , where $T ♯ μ$ is the‌ push-forward measure of $μ$ by $T$ . Interestingly,‌ when $μ$ and $ν$ are empirical measures, i.e.,‌ $μ = \frac{1}{N} \sum_{n = 1‌} δ_{x_{n}}$ and $ν = \frac{1}{N‌} \sum_{n = 1} δ_{y_{n}}$ ,‌ a transport map is nothing more than a‌ matching between ${{x}_{n}}$ and ${‌ y_{n}}$ that minimizes the cost ${\sum‌}_{n} c ({x}_{n}, T (‌ x_{n}))$ .

Recently, optimal transport‌ gained a lot of interest in the ML‌ community 114 thanks to its application to images‌ and to new techniques to compute approximate matchings‌ in a tractable way 117. Even more‌ unexpected applications of optimal transport have been discovered:‌ to protect privacy 53, fairness 46,‌ etc. Those connections are promising, but only primitive‌ for the moment. For instance, consider the problem‌ of matching students to schools. The unfairness level‌ of a school can be measured as the‌ Wasserstein distance between the distribution of the students‌ within that school compared to the overall distribution‌ of students. Then the matching algorithms could have a constraint of minimizing‌ the sum of (or‌ its maximum) unfairness levels;‌‌ alternatively, we could aim at designing mechanisms giving‌ incentives to schools to‌ be fair in their‌‌ allocation (or at least in their list preferences),‌ typically by paying a‌ higher fee if the‌‌ unfairness level is high.

2.3 General objectives

The‌ overarching objective of FAIRPLAY‌ of to create algorithms‌‌ to learn for and with users—and techniques to‌ analyze them—, through‌ the study of multi-agent‌‌ learning systems where the agents can be cooperatively‌ or competitively learning agents,‌ or agents providing or‌‌ generating data, while guaranteeing that fairness and‌ privacy constraints are satisfied‌ for the involved users.‌‌ We detail this global objective into a number‌ of more specific ones.‌

Objective 1: Developing‌‌ fair and private mechanisms

Our first objective is‌ to incorporate ethical aspects‌ of fairness and privacy‌‌ in mechanisms used in typical problems occurring in‌ Internet applications, in particular‌ auctions, matching, and recommendation.‌‌ We will focus on social welfare and consider‌ realistic cases with multiple‌ agents and sequential learning‌‌ that occur in practice due to sequential decision‌ making. Our objective is‌ both to construct models‌‌ to analyze the problem, to devise algorithms that‌ respect the constraints at‌ stake, and to evaluate‌‌ the different trade-offs in standard notions of utility‌ introduced by ethical constraints.‌
Objective 2: Developing‌‌ multi-agent statistics and learning

Data is now acquired,‌ treated and/or generated by‌ a whole network of‌‌ agents interacting with the environment. There are also‌ often multiple agents learning‌ either collaboratively or competitively.‌‌ Our second objective is to build a new‌ set of tools to‌ perform statistics and learning‌‌ tasks in such environments. To this end, we‌ aim at modeling these‌ situations as multi-agent systems‌‌ and at studying the dynamics and equilibrium of‌ these complex game-theoretic situations‌ between multiple learning algorithms‌‌ and data providers.
Objective 3: Improving the‌ theoretical state of the‌ art

Research must rely‌‌ on theoretical, proven guarantees. We develop new results‌ for the techniques introduced‌ before, such as prophet‌‌ inequalities, (online) matchings, bandits and RL, etc.
Objective‌ 4: Proposing practical‌ solutions and enhancing transfer‌‌ from research to industry

Our last scientific objective‌ is to apply and‌ implement theoretical works and‌‌ results to practical cases. This will be a‌ crucial component of the‌ project as we focus‌‌ on transfer within Criteo.
Objective 5: Scientific‌ Publications

We aim at‌ publishing our results in‌‌ top-tier machine learning conferences (NeurIPS, ICML, COLT, ICLR,‌ etc.) and in top-tier‌ game theory journals (Games‌‌ and Economic Behavior, Mathematics of OR, etc.). We‌ will also target conferences‌ at the junction of‌‌ those fields (EC, WINE, WebConf, etc.) as well‌ as conferences specifically on‌ security and privacy (IEEE‌‌ S&P, NDSS, CSS, PETS, etc.) and on fairness‌ (FAccT, AIES).

All the‌ five objectives are interlaced.‌‌ For instance, fairness and privacy constraints are important‌ in Objective 2 whereas‌ the multi-agent aspect is‌‌ also important in Objective‌ 1. Objectives 4 and 5 are transversal and‌ present in all the first three objectives.

3‌ Research program

To reach the objectives laid out‌ above, we organize the research in three themes.‌ The first one focuses on developing fair mechanisms.‌ The second one considers private mechanisms, and in‌ particular considers the challenge of reconciling fairness and‌ privacy—which are often conflicting notions. The last theme,‌ somewhat transverse to the first two, consists in‌ leveraging/incorporating structure in all those problems in order‌ to speed up learning. Of course, all themes‌ share common points on both the problems/applications considered‌ and the methods and tools used to tackle‌ them; hence there are cross-fertilization between the different‌ themes.

3.1 Theme 1: Developing fair mechanisms for‌ auctions and matching problems

3.1.1 Fairness in auction-based‌ systems

Online ads platforms are nowadays used to‌ advertise not just products, but also opportunities such‌ as jobs, houses, or financial services. This makes‌ it crucial for such platforms to respect fairness‌ criteria (be it only for legal reasons), as‌ an unfair ad system would deprive a part‌ of the population of some potentially interesting opportunities.‌ Despite this pressing need, there is currently no‌ technical solution in place to provably prevent discriminations.‌ One of the main challenge is that ad‌ impression decisions are the outcome of an auction‌ mechanism that involves bidding decisions of multiple self-interested‌ agents controlling only a small part of the‌ process, while group fairness notions are defined on‌ the outcome of a large number of impressions.‌ We propose to investigate two mechanisms to guarantee‌ fairness in such a complex auction-based system (note‌ that we focus on online ad auctions but‌ the work has broader applicability).

Advertiser-centric (or bidder-centric)‌ fairness

We first focus on advertiser-centric fairness, i.e.,‌ the advertiser of a third-party needs to make‌ sure that the reached audience is fair independently‌ of the ad auction platform. A key difficulty‌ is that the advertiser does not control the‌ final decision for each ad impression, which depends‌ on the bids of other advertisers competing in‌ the same auction and on the platform's mechanism.‌ Hence, it is necessary that the advertiser keeps‌ track of the auctions won for each of‌ the groups and dynamically adjusts its bids in‌ order to maintain the required balance.

A first‌ difficulty is to model the behavior of other‌ advertisers. We can first use a mean-field games‌ approach similar to 86 that approximates the other‌ bidders by an (unknown) distribution and checks equilibrium‌ consistency; this makes sense if there are many‌ bidders. We can also leverage refined mean-field approximations‌ 80 to provide better approximations for smaller numbers‌ of advertisers. Then a second difficulty is to‌ find an optimal bidding policy that enforces the‌ fairness constraint. We can investigate two approaches. One‌ is based on an MDP (Markov Decision Process)‌ that encodes the current fairness level and imposes‌ a hard constraint. The second is based on modeling the problem as‌ a contextual bandit problem.‌ We note that in‌‌ addition to fairness constraints, privacy constraints may complicate‌ the optimal solution finding.‌
Platform-centric (or auction-centric) fairness‌‌

We also consider the problem from the platform's‌ perspective, i.e., we assume‌ that it is the‌‌ platform's responsibility to enforce the fairness constraint. We‌ also focus here on‌ demographic parity. To make‌‌ the solution practical, we do not consider modification‌ of the auction mechanism,‌ instead we consider a‌‌ given mechanism and let the platform adapt dynamically‌ the bids of each‌ advertiser to achieve the‌‌ fairness guarantee. This approach would be similar to‌ the pacing multipliers used‌ by some platforms 66‌‌, 65, but using different multipliers for‌ the different groups (i.e.,‌ different values of the‌‌ sensitive attribute).

Following recent theoretical work on auction‌ fairness 59, 85‌, 63 (which assumes‌‌ that the targeted population of all ads is‌ known in advance along‌ with all their characteristics),‌‌ we can formulate fairness as a constraint in‌ an optimization problem for‌ each advertiser. We study‌‌ fairness in this static auction problem in which‌ the auction mechanism is‌ fixed (e.g., to second‌‌ price). We then move to the online setting‌ in which users (but‌ also advertisers) are dynamic‌‌ and in which decisions must be taken online,‌ which we approach through‌ dynamic adjustment of pacing‌‌ multipliers.

3.1.2 Fairness in matching and selection problems‌

In this second part,‌ we study fairness in‌‌ selection and matching problems such as hiring or‌ college admission. The selection‌ problem corresponds to any‌‌ situation in which one needs to select (i.e.,‌ assign a binary label‌ to) data examples or‌‌ individuals but with a constraint on the number‌ of maximum number of‌ positive labels. There are‌‌ many applications of selection problems such as police‌ checks, loan approvals, or‌ medical screening. The matching‌‌ problem can be seen as the more general‌ variant with multiple selectors.‌ Again, a particular focus‌‌ is put here on cases involving repeated selection/matching‌ problems and multiple decision‌ makers.

Fair repeated multistage‌‌ selection

In our work 74, we identified‌ that a key source‌ of discrimination in (static)‌‌ selection problems is differential variance, i.e., the‌ fact that one has‌ quality estimates that have‌‌ different variances for different groups. In practice, however,‌ the selection problem is‌ often ran repeatedly (e.g.,‌‌ at each hiring campaign) and with partial (and‌ increasing) information to exploit‌ for making decisions.

Here,‌‌ we consider the repeated multistage selection problem, where‌ at each round a‌ multistage selection problem is‌‌ solved. A key aspect is that, at the‌ end of a round,‌ one learns extra information‌‌ about the candidates that were selected—hence one can‌ refine (i.e., decrease the‌ variance of) the quality‌‌ estimate for the groups in which more candidates‌ were selected. We will‌ first rethink fairness constraints‌‌ in this type of repeated decision making problems.‌ Then we will both‌ study the discrimination that‌‌ come out of natural‌ (e.g., greedy) procedure as well as design (near)‌ optimal ones for the constraints at stake. We‌ also investigate how the constraints affect the selection‌ utility.
Multiple decision-makers
Next, we investigate cases with‌ multiple decision-makers. We propose two cases in particular.‌ The first one is the simple two-stage selection‌ problem but where the decision-maker doing the first-stage‌ selection is different from the decision-maker doing the‌ second-stage selection. This is a typical case for‌ instance for recruiting agencies that propose sublists of‌ candidates to different firms that wish to hire.‌ The second case is when multiple-decision makers are‌ trying to make a selection simultaneously—a typical example‌ of this being the college admission problem (or‌ faculty recruitment). We intend to model it as‌ a game between the different colleges and to‌ study both static solutions as well as dynamic‌ solutions with sequential learning, again modeling it as‌ a bandit problem and looking for regret-minimizing algorithms‌ under fairness constraints. A number of important questions‌ arise here: if each college makes its selection‌ independently and strategically (but based on quality estimates‌ with variances that differ amongst groups), how does‌ it affect the “global fairness” metrics (meaning in‌ aggregate across the different colleges) and the “local‌ fairness” metrics (meaning for an individual college)? What‌ changes if there is a central admission system‌ (such as Parcoursup)? And in this later case,‌ how to handle fairness on the side of‌ colleges (i.e., treat each college fairly in some‌ sense)?
Fair matching with incentives in two-sided platforms‌

We will study specifically the case of a‌ platform matching demand on one side to offer‌ on the other side, with fairness constraints on‌ each side. This is the case for instance‌ in online job markets (or crowdsourcing). This is‌ similar to the previous case but, in addition,‌ here there is an extra incentives problem: companies‌ need to give the right incentives to job‌ applicants to accept the jobs; while the platform‌ doing the match needs to ensure fairness on‌ both sides (job applicants and companies). This gives‌ rise to a complicated interplay between learning and‌ incentives that we will tackle again in the‌ repeated setting.

We finally mention that, in many‌ of these matching problems, there is an important‌ time component: each agent needs to be matched‌ “as soon as possible”, yielding a trade-off between‌ the delay to be matched and the quality‌ of the match. There is also a problem‌ of participation incentives; that is how the matching‌ algorithm used affect the behavior of the participants‌ in the matching “market” (participation or not, information‌ revelation, etc.). In the long-term, we will incorporate‌ these aspects in the above models.

Throughout the‌ work in this theme, we will also consider‌ a question transverse and present in all the‌ models above: how can we handle multidimensional fairness‌, that is, where there are multiple sensitive‌ attributes and consequently an exponential number of sub-groups defined by all intersections;‌ this combinatorial is challenging‌ and, for the moment,‌‌ still exploratory.

3.2 Theme 2: Reconciling, and enforcing‌ privacy with fairness

In‌ the previous theme, we‌‌ implicitly assumed that we know the users' group,‌ i.e., their sensitive attributes‌ such as gender, age,‌‌ or ethnicity. In practice, one of the key‌ question when implementing fairness‌ mechanisms is how to‌‌ measure/control fairness metrics without having access to these‌ protected attributes. This question‌ relates to the link‌‌ between privacy and fairness and the trade-off between‌ them (as fairness requires‌ data and privacy tends‌‌ to protect it) 118, 68.

A‌ first option to solve‌ this problem would be‌‌ (when it is possible) to build proxies 81‌, 121 for protected‌ attributes using available information‌‌ (e.g., websites visited or products bought) and to‌ measure or control for‌ fairness using those in‌‌ place of the protected attributes. As the accuracy‌ of these proxies cannot‌ be assessed, however, they‌‌ cannot be used for any type of “public‌ certification”—that is, for a‌ company to show reasonable‌‌ fairness guarantees to clients (e.g., as a commercial‌ argument), or (even less)‌ to regulators. Moreover, in‌‌ many cases, the entity responsible for fairness should‌ not be accessing sensitive‌ information, even through proxies,‌‌ for privacy reasons.

In FAIRPLAY, we investigate a‌ different means of certifying‌ fairness of decisions without‌‌ having access to sensitive attributes, by partnering with‌ a trusted third-party that‌ collects protected attributes (that‌‌ could for instance be a regulator or a‌ public entity, such as‌ Nielsen, say). We distinguish‌‌ two cases:

If the third-party and the company‌ share a common identifier‌ of users, then computing‌‌ the fairness metric without leaking information to each‌ other will boil down‌ to a problem of‌‌ secure multi-party computation (SMC). In such a case,‌ there could be a‌ need to be able‌‌ to learn, which opens the topic of learning‌ and privacy under SMC.‌ This scenario, however, is‌‌ likely not the most realistic one as having‌ a common identifier requires‌ a deep technical integration.‌‌
If the third-party and the company do not‌ share a common identifier‌ of users, but there‌‌ are common features that they both observe 89‌, then it is‌ possible only to partially‌‌ identify the joint distribution. With additional structural assumptions‌ on the distribution, however,‌ it could be identified‌‌ accurately enough to estimate biases and fairness metrics.‌ This becomes a distribution‌ identification problem and brings‌‌ a number of questions such as: how to‌ do the distribution identification?‌ how to optimally query‌‌ data from the third party to train fair‌ algorithms with high utility?‌ etc. An important point‌‌ to keep in mind in such a study‌ is that it is‌ likely that the third‌‌ party user-base is different from that of the‌ company. It will therefore‌ be key to handle‌‌ the covariate shift from one distribution to the‌ other while estimating biases.‌

This distribution identification problem‌‌ will be important in‌ the context of privacy, even independently of fairness‌ issues. Indeed, in the near future, most learning‌ will happen in a privacy-preserving setting (for instance,‌ because of the Chrome privacy sandbox). This will‌ require new learning schemes (different from e.g., Empirical‌ Risk Minimization) as samples from the usual joint‌ distribution $(X, Y)$ of samples/labels‌ will no longer be observed. Only aggregated data—e.g.,‌ (empirical) marginals of the form $E [Y‌ | X_{2} = 4, X_{7‌} = ``lemonde.fr'']$ —will be observed, with a‌ limited budget of requests. This also brings questions‌ such as how to mix it with ERM‌ on some parts of the traffic, what is‌ the (certainly adaptive or active) optimal strategy to‌ query the marginals, etc. This problem will be‌ further complicated by the fact that privacy (for‌ instance through the variety of consents) will be‌ heterogeneous: all features are not available all the‌ time. This is therefore strongly related to learning‌ with missing features and imputation 88.

In‌ relation to the above problems, a key question‌ is to determine what is the most appropriate‌ definition of fairness to consider. Recall that it‌ is well-known that usual fairness metrics are not‌ compatible 93. Moreover, in online advertising, fairness‌ can be measured at multiple different levels: at‌ the level of bids, at the level of‌ audience reached, at the level of clicking users,‌ etc. Fairness at the level of bids does‌ not imply fairness of the audience reached (see‌ Theme 1); yet external auditors would measure which‌ ad is displayed—as was done for some ad‌ platforms 122—hence in terms of public image,‌ that would be the appropriate level to define‌ fairness. Intimately, the above problem relates to the‌ question of measuring what is the relevant audience‌ of an ad, which would define the label‌ if one were to use the EO fairness‌ notion. This label is typically not available. We‌ can explore three ways to overcome this issue.‌ The first is to find a sequential way‌ to learn the label through users clicking on‌ ads. The second and third options are to‌ focus in a first step on individual fairness,‌ or on counterfactual fairness 96, which has‌ many possible different level of assumptions and was‌ popularized in 2020 97. The notion of‌ counterfactual is key in causality 116. A‌ model is said counterfactually fair if its prediction‌ does not change (too much) by intervening on‌ the sensitive attribute. Several works already propose ways‌ of designing models that are counterfactually fair 92‌, 129, 128. This seems to‌ be quite an interesting, but challenging direction to‌ follow.

Finally, an alternative direction would be to‌ purse modeling the trade-off between privacy and fairness.‌ For instance, in some game theoretic models, users‌ can choose the quantity of data that they‌ reveal 79, 53, so that the objective functions integrate different‌ levels of fairness/privacy. Then‌ those models model should‌‌ be studied both in terms of equilibrium and‌ in the online setup,‌ with the objective of‌‌ identifying how the strategic privacy considerations affect the‌ fairness-utility tradeoff.

3.3 Theme‌ 3: Exploiting structure in‌‌ online algorithms and learning problems

Our last research‌ direction is somewhat transverse,‌ with possible application to‌‌ improving algorithms in the first three themes. We‌ explore how the underlying‌ structure can be exploited,‌‌ in the online and learning problems considered before,‌ to improve performance. Note‌ that, in all these‌‌ problems, we will incorporate the fairness and privacy‌ aspects even if they‌ are somewhat transverse to‌‌ the structure considered.1 The following sections are‌ illustrating examples on how‌ hidden structure can be‌‌ leveraged in specific examples.

3.3.1 Leveraging structure in‌ online matching

Finding large‌ matchings in graphs is‌‌ a longstanding problem with a rich history and‌ many practical and theoretical‌ applications 107, 84‌‌, 44, 43. Recall that given‌ a graph $G =‌ (𝒱, ℰ‌‌)$ —where $𝒱$ is a set of vertices‌ and $ℰ$ is a‌ set of edges—, a‌‌ matching $ℳ \subset ℰ$ is a subset of‌ edges such that each‌ vertex belongs to at‌‌ most one edge $e \in ℳ$ . In‌ that context, a perfect‌ matching $ℳ$ is a‌‌ matching where each vertex $v \in 𝒱$ is‌ associated to an edge‌ $e \in ℳ$ ,‌‌ and a maximum matching is a matching of‌ maximum size (one can‌ also consider weights on‌‌ edges). Here, we study an online setting, which‌ is more adequate in‌ applications such as Internet‌‌ advertising where ad impressions must be assigned to‌ available ad slots 107‌, 56. Consider‌‌ a bipartite graph, where $𝒱 = U \cup‌ V$ is the union‌ of two disjoints sets.‌‌ Nodes $u \in U$ are known beforehand, whereas‌ nodes $v \in V‌$ are discovered one at‌‌ a time, along with the edges they belong‌ to, and must be‌ either immediately matched to‌‌ an available (i.e., unmatched yet) vertex $u \in‌ U$ or discarded. Online‌ bipartite matching is relevant‌‌ in two-sided markets besides ad allocations such as‌ assigning tasks to workers‌ 84.

A natural‌‌ measure for the quality of an online matching‌ algorithm is the “competitive‌ ratio” (CR): the ratio‌‌ between the size of the created matching to‌ the size of the‌ optimal one 107.‌‌ The seminal work 91 introduced an optimal algorithm‌ for the adversarial case‌ 47, that guarantees‌‌ a CR of $1 - \frac{1}{e}$ ;‌ but focusing on a‌ pessimistic worst-case. In practice,‌‌ some relevant knowledge (either given a priori or‌ learned during the process)‌ on the underlying structure‌‌ of the problem can be leveraged. The focus‌ then shifted to models‌ taking into account some‌‌ type of stochasticity in the arrival model, mostly‌ for the i.i.d. model‌ where arriving vertices $v‌‌ \in V$ are drawn‌ from a fixed distribution $𝒟$ 87, 45‌, 75, 90, 102, 103‌. The classical approach consists in optimizing the‌ CR over the distribution $𝒟$ . Even in‌ this seemingly optimistic framework, however, it is now‌ known that there is no hope for a‌ CR of more than 0.823 103. Moreover,‌ this generally leads to very large linear programs‌ (LP).

A more recent approach restricts the distribution‌ $𝒟$ over which the problem is optimized to‌ classes of graphs with an underlying stochastic structure.‌ The benefit of this approach is two-fold: it‌ gives hope for higher competitive ratios, and for‌ simpler algorithms. Experiments also proved that complex algorithms‌ optimized on $𝒟$ fared no better than simple‌ greedy heuristics on “real-life” graphs 51. A‌ few results along these lines show that is‌ a promising path. For instance, 56 studied the‌ problem on graphs where each vertex has degree‌ at least $d$ and found a competitive ratio‌ of $1 - {( 1 - 1 /‌ d)}^{d}$ . On d-regular graphs, 64‌ designed a $1 - O (\sqrt{log d‌} / \sqrt{d})$ competitive algorithm. 105 showed that‌ greedy algorithms were highly efficient on Erdös-Renyi random‌ graphs, with a competitive ratio of 0.837 in‌ the worst case. 42 showed that in a‌ specific market with two types of matching agents,‌ the behavior of the matching algorithm varies with‌ the homogeneity of the market. Our goal here‌ is to go beyond the independence assumption underlying‌ all these works.

Introducing correlation and inhomogeneity

We‌ will start by deriving and studying optimal online‌ matching strategies on widely studied classes of graphs‌ that present simple inhomongeneity or correlation structures (which‌ are often present in applications). The stochastic block‌ model 39 is often used to generate graphs‌ with an underlying community structure. It presents a‌ simple correlation structure: two vertices in the same‌ community are more likely to have a common‌ neighbors than two vertices in different communities. Another‌ focus point will be a generalized version of‌ the Erdös-Renyi model, where the inplace vertices $u‌ \in 𝒰$ are divided into sets $s_{i‌}$ , where $u \in s_{i}$ generates an‌ edge with probability ${p}_{i} = \frac{c_{i‌}}{n}$ . These two settings should give us‌ a better understanding of how heterogeneity and correlation‌ affect the matching performance.

Deriving the competitive ratio‌ implies to study the asymptotic size of maximum‌ matchings in random graphs. Two methods are usually‌ used. The first and constructive one is the‌ study of the Karp-Sipser algorithm on the graph‌ 48. The second one involves the rich‌ theory of graph weak local convergence 49.‌ A straightforward application of the methods, however, requires‌ the graph to have independence properties; adapting them‌ to graphs with a correlation structure will require‌ new ideas.
Configuration models and random geometric graphs‌
A configuration model is described as follows (in the bi-partite case). Each‌ vertex $u \in 𝒰‌$ has a number of‌‌ half-edges drawn for the same distribution $π_{𝒰‌}$ and each vertex $v‌ \in 𝒱$ has a‌‌ number of half-edges drawn from $π_{V}$ (with‌ the assumption that the‌ expected total numbers of‌‌ half edges from $𝒰$ and $𝒱$ are the‌ same). Then a vertex‌ $v \in 𝒱$ that‌‌ arrives in the sequential fashion has its half-edges‌ “completed” by a (still‌ free) half-edge of $𝒰‌‌$ . This is a standard way of creating‌ random graphs with (almost)‌ fixed distribution of degrees.‌‌ Here the question would simply be the competitive‌ ratio of some greedy‌ algorithm, whether the distributions‌‌ $π_{𝒰}$ and ${π}_{𝒱}$ are known beforehand‌ or learned on the‌ fly. An interesting variant‌‌ of this problem would be to assume the‌ existence of a (hidden‌ or not) geometric graph.‌‌ Each $u \in 𝒰$ is drawn i.i.d in‌ $ℛ^{d}$ (say a‌ Gaussian centered at 0)‌‌ and similarly for $v \in 𝒱$ . Then‌ there is an edge‌ between $u$ and $v‌‌$ with a probability depending on the distance between‌ them. Here again, interesting‌ variants can be explored‌‌ depending on whether the distribution is known or‌ not, and whether the‌ locations of $u$ and/or‌‌ $v$ are observed or not.
Learning while matching‌
In practical applications, the‌ full stochastic structure of‌‌ the graphs may not be known beforehand. This‌ begs the question: what‌ will happen to the‌‌ performance of the algorithms if the graph parameters‌ are learned while matching?‌ In the generalized Erdös-Renyi‌‌ graph, this will correspond to learning the probability‌ of generating edges. For‌ the stochastic block model,‌‌ the matching algorithm will have to perform online‌ community detection.

3.3.2 Exploiting‌ side-information in sequential learning‌‌

We end with an open direction that may‌ be relevant to many‌ of the problems considered‌‌ above: how to use side-information to speed-up learning.‌ In many sequential learning‌ problems where one receives‌‌ feedback for each action taken, it is actually‌ possible to deduce, for‌ free, extra information from‌‌ the known structure of the problem. However, how‌ to incorporate that information‌ in the learning process‌‌ is often unclear. We describe it through two‌ examples.

One-sided feedback in‌ auctions
In online ad‌‌ auctions, the advertisers' strategy is to bid in‌ a compact set of‌ possible bids. After placing‌‌ a bid, the advertiser learns whether they won‌ the auction or not;‌ but even if they‌‌ do not observe the bids of other advertisers,‌ they can deduce for‌ free some extra information:‌‌ if they win they learn that they would‌ have won with any‌ higher bid and if‌‌ they loose they learn that they would have‌ lost with any lower‌ bid 126, 60‌‌. We will investigate how to incorporate this‌ extra information in RL‌ procedures devised in Theme‌‌ 1. One option is by leveraging the Kaplan-Meier‌ estimator.
Side-information in dynamic‌ resource allocation problems and‌‌ matching
Generalizing the idea‌ above, one can observe side-information in many other‌ problems 41. Typically, in resource allocation problems‌ (e.g., how to allocate a budget of ad‌ impressions), one can leverage a monotony property: one‌ would not have gained more by allocating less.‌ Similarly, in matching with unknown weights, it is‌ often possible upon doing a particular match to‌ learn the weight of other potential pairs.

4‌ Application domains

4.1 Typical problems and use-cases

In‌ FAIRPLAY, we focus mainly on problems involving learning‌ that relate to Internet applications. We will tackle‌ generic problems (in particular auctions and matching) with‌ different applications in mind, in particular some applications‌ in the context of Criteo's business but also‌ others. A crucial property of those applications is‌ the aforementioned ethical concerns, namely fairness and/or privacy.‌ The team was shaped and motivated by several‌ such use-cases, from more practical (with possible short‌ or middle term applications in particular in Criteo‌ products) to more theoretical and exploratory ones. We‌ describe first here the main types of generic‌ problems and use-cases considered in this context.

Auctions‌ 95

There are many different types of auctions‌ that an agent can use to sell one‌ or several items in her possession to $n‌$ potential buyers. This is the typical way in‌ which spots to place ads are sold to‌ potential advertisers. In case of a single item,‌ the seller ask buyers to bid $b_{i‌} \in [0, 1]$ on the‌ item and the winner of the item is‌ designating via an “allocation rule” that maps bids‌ $b \in {[0, 1]}^{n‌}$ to a winner in ${0, ...‌, n}$ (0 refers to the no‌ winner case). Then the payment rule $p :‌ {[0, 1]}^{n} \to {[‌ 0, 1]}^{n}$ indicates the amount‌ of money that each bidder must pay to‌ the seller. Auctions are specific cases of a‌ broader family of “mechanisms”. Knowing the allocation and‌ payment rules, bidders have incentives to bid strategically.‌ Different auctions (or rules) end up with different‌ revenue to the seller, who can choose the‌ optimal rules. This is rather standard in economics,‌ but these interactions become way more intricate when‌ repeated over time (as in the online ad‌ market 111), when several items are sold‌ at the same time (for instance in bundles),‌ when the buyers have partial information about the‌ actual value of the item 126 and/or reciprocally‌ when the seller does not know the value‌ distributions of the buyer. In that case, she‌ might be tempted to try to learn them‌ from the previous bids in order to design‌ the optimal mechanism. Knowing this, the bidders have‌ incentives to long term strategic behaviors, ending up‌ in a quite complicated game between learning algorithms‌ 112. This setting of interacting algorithms is‌ actually of interest by itself, irrespectively of ad auctions. It is noteworthy‌ also that traditional auction‌ mechanisms do not guarantee‌‌ any fairness notion and that the literature on‌ fixing that (for applications‌ where it matters) is‌‌ only nascent 59, 110, 63,‌ 85.

Matching 94‌, 109

A matching‌‌ is nothing more than a bi-partite graph between‌ some agents (patients, doctors,‌ students) and some resources‌‌ (respectively, organs, hospital, schools). The objective is to‌ figure out what is‌ the “optimal” matching for‌‌ a given criterion. Interestingly, there are two different—and‌ mostly unrelated yet—concepts of‌ “good matching”. The first‌‌ one is called “stable” in the sense that‌ each agent expresses preferences‌ over resources (and vice-versa)‌‌ and be such that no couple (agent-resource) that‌ are un-paired would prefer‌ to be paired together‌‌ than with their current paired resource/agent. In the‌ other concept of matching,‌ agents and resources are‌‌ described by some features (say vectors in ${R‌}^{d}$ , denoted by‌ $a_{n}$ for agents‌‌ and $r_{m}$ for resources) and pairing ${a‌}_{n}$ to $r_{m‌}$ incurs a cost of‌‌ $c (a_{n}, r_{m})‌$ , for some a‌ given function $c :‌‌ {(R^{d})}^{2} \to [0‌, 1]$ .‌ The objective is then‌‌ to minimize the total cost of the matching‌ $\sum_{n} c (‌ a_{n}, {r‌‌}_{σ (n)})$ , where $σ‌ (n)$ is‌ the resource allocated to‌‌ agent $n$ .

Matching is used is many‌ different applications such as‌ university admission (e.g., in‌‌ Parcoursup). Notice that strategic interactions arise in matching‌ if agents or resources‌ can disclose their preferences/features‌‌ to each other. Learning is also present as‌ soon as not everything‌ is known, e.g., the‌‌ preferences or costs. Many applications of matching (again,‌ such as college admission)‌ are typical examples where‌‌ fairness and privacy are of utmost importance. Finally,‌ matching is also at‌ the basis of several‌‌ Internet applications and Criteo products, for instance to‌ solve the problem of‌ matching a given ad‌‌ budget to fixed ad slots.

Ethical notions in‌ those use-cases

In both‌ problems, individual users are‌‌ involved and there is a clear need to‌ consider fairness and privacy.‌ However, the precise motivation‌‌ and instantiation of these notions depends on the‌ specific use-case. In fact,‌ it is often part‌‌ of the research question to decide which are‌ the most relevant fairness‌ and privacy notions, as‌‌ mentioned in Section 2.1. We will throughout‌ the team's life put‌ an important focus on‌‌ this question, as well as on the question‌ of the impact of‌ the chosen notion on‌‌ performance.

4.2 Application areas

In FAIRPLAY, we consider‌ both applications to Criteo‌ use-cases (online advertisement) and‌‌ other applications (with other appropriate partners).

4.2.1 Online‌ advertisement

Online advertising offers‌ an application area for‌‌ all of the research themes of FAIRPLAY; which‌ we investigate primarily with‌ Criteo.

First, online advertising‌‌ is a typical application‌ of online auctions and we consider applications of‌ the work on auctions to Criteo use-cases, in‌ particular the work on advertiser-centric fairness where the‌ advertiser is Criteo. From a practical point of‌ view, privacy will have to be enforced in‌ such applications. For instance, when information is provided‌ to advertisers to define audiences or to visualize‌ the performance of their campaigns (insights) there is‌ a possibility of leaking sensitive information on users.‌ In particular, excellent proxies on protected attributes should‌ probably not be leaked to advertisers, or transformed‌ before (e.g., with the differential privacy techniques). This‌ is therefore also an application of the fairness-vs-privacy‌ research thread.

Note that, even before considering those‌ questions, the first very important theoretical question is‌ to determine what is the more appropriate definition‌ of fairness (as there are, as mentioned above,‌ many different variations) in those applications. We recall‌ that it is well-known that usual fairness metrics‌ are not compatible 93. Moreover, in online‌ advertising, fairness can be measured in term of‌ bidding and recommendation or in term of what‌ ads are actually displayed. Being fair on bidding‌ does not lead to fairness in ads displaying‌ 110, mainly because of the other advertising‌ actors. While fairness in bidding and/or recommendation seem‌ the most important because they only rely on‌ our models, external auditors can easily get data‌ on which ads we display.

We will also‌ investigate applications of fair matching techniques to online‌ advertsing and to Criteo matching products—namely retargeting (personalized‌ ads displayed on a website) and retail media‌ (sponsored products on a merchant website). Indeed, one‌ of Criteo major products, retail media can be‌ cast as an online matching problem. On a‌ given e-commerce website (say, target), several advertisers—currently brands—are‌ running campaigns so that their products are “sponsored”‌ or “boosted”, i.e., they appear higher on the‌ list of results of a given query. The‌ budgets (from a Criteo perspective) must be cleared‌ (daily, monthly or annually). This constraint is easy‌ thanks to the high traffic, but the main‌ issue is that, without control/pacing/matching in times, the‌ budget is depleted after only a few hours‌ on a relatively low quality traffic (i.e., users‌ that generate few conversions hence a small ROI‌ for the advertisers). The question is therefore whether‌ an individual user should be matched or not‌ to boosted/sponsored products at a given time so‌ that the ROI of the advertisers is maximized,‌ the campaign budget is depleted and the retailer‌ does not suffer too much from this active‌ corruption of its organic results. Those are three‌ different and concurrent objectives (for respectively the advertisers,‌ Criteo and the retailers) that must be somehow‌ conciliated. This problem (and more generally this type‌ of problems) offers a rich application area to‌ the FAIRPLAY research program. Indeed, it is crucial‌ to ensure that fairness and privacy are respected.‌ On the other hand, users, clicks, conversion arrival are not “worst case”.‌ They rather follow some‌ complicated—but certainly learnable—process; which‌‌ allows applying our results on exploiting structure.

4.2.2‌ “Physical matching”

We investigate‌ a number of other‌‌ applications of matching: assignment of daycare slots to‌ kids, mutation of professors‌ to different academies, assignment‌‌ of kidneys to patients, assignment of job applicants‌ to jobs. In all‌ these applications, there are‌‌ crucial constraints of fairness that complicate the matching.‌ We leverage existing partnership‌ with the CNAF, the‌‌ French Ministry of Education and the Agence de‌ la biomédecine in Paris‌ for the first three‌‌ applications; for the last we will consolidate a‌ nascent partnership with Pole‌ Emploi and LinkedIn.

5‌‌ Highlights of the year

5.1 Awards

Mariia Vladimirova‌ was named “Rising Star‌ in AI Ethics” issued‌‌ by Women in AI Ethics organization (March 2025).‌

Vianney Perchet and Patrick‌ Loiseau were named Hi!‌‌ Paris fellows in 2025. They will lead a‌ synergy fellowship on “Design,‌ Incentivization, Optimization and (Reinforcement-‌‌ )Learning of Multi-Layered Market” together with two economists:‌ Yuki Tamura and Pablo‌ Winant.

Marc Abeille and‌‌ Lorenzo Croissant (with co-authors) received an best paper‌ award at ALT’25.

6‌ New results

6.1 Auctions‌‌ and mechanism design

Participants: Benjamin Heymann, Vianney‌ Perchet, Maxime Vono‌.

In 17,‌‌ we compare uniform price and discriminatory multi-unit auctions‌ through the lens of‌ regret minimization. By framing‌‌ the strategic participation of bidders as an online‌ learning problem, we establish‌ new theoretical performance bounds‌‌ for these fundamental pricing rules.

We also deeply‌ investigate settings with explicit‌ constraints. In 2,‌‌ we study competitive and revenue-optimal pricing when buyers‌ have budget limits, and‌ in 12, we‌‌ tackle the problem of selling multiple complements with‌ packaging costs, designing mechanisms‌ that account for the‌‌ overhead of combinatorial bundling. In 30, we‌ explore side-by-side first-price auctions‌ in the presence of‌‌ imperfect bidders, extending the understanding of bidder behavior‌ in decentralized environments. Finally,‌ to support applied research‌‌ in this space, we introduce CriteoPrivateAds in 31‌, a real-world bidding‌ dataset specifically designed to‌‌ help the community build and test private advertising‌ systems.

6.2 Matching markets‌

Participants: Matthieu Lerasle,‌‌ Patrick Loiseau, Simon Mauras, Vianney Perchet‌.

In 5,‌ we provide a direct‌‌ mathematical proof of the short-side advantage in random‌ matching markets, formally demonstrating‌ why the side with‌‌ fewer participants in unbalanced bipartite markets inherently secures‌ vastly superior matches.

We‌ expand the scope of‌‌ classical matching by introducing realistic structural constraints. In‌ 1, we explore‌ the correlation of rankings‌‌ in matching markets, analyzing how interdependent participant preferences‌ impact stability and efficiency.‌ In 26, we‌‌ investigate two-sided matching under resource-regional caps, and in‌ 25, we focus‌ on the theoretical properties‌‌ of optimal unimodular matchings. Applying these matching frameworks‌ to real-world policy, we‌ analyze college admissions in‌‌ France in 32, evaluating the structural impacts‌ of affirmative action, overlapping‌ reserves, and housing quotas.‌‌

6.3 Bandits, reinforcement learning,‌ and games

Participants: Benjamin Heymann, Vianney Perchet‌, Hugo Richard.

We significantly advanced the‌ theory of learning in games and multi-agent systems.‌ In 20, accepted at AAMAS 2025, we‌ study learning in games with progressive hiding, proposing‌ algorithms that allow agents to strategically conceal information‌ while maintaining efficiency. In 11, we solve‌ the “harder path” of achieving last-iterate convergence for‌ uncoupled learning in zero-sum games with bandit feedback,‌ proving that independent learners can reach equilibrium even‌ with limited feedback.

Our work on bandit frameworks‌ spans numerous complex environments. In 4, we‌ propose robust algorithms for adversarial bandits facing arbitrary‌ strategies. For combinatorial and infinite action spaces, we‌ developed oracle-efficient combinatorial semi-bandits 14, established methods‌ for tracking significant shifts in infinite-armed bandits 18‌, and extended linear bandits beyond inner product‌ spaces utilizing bandit optimal transport 23. We‌ also study bandits under strict constraints, such as‌ multi-armed bandits with minimum aggregated revenue constraints 21‌ and offline contextual bandits with counterfactual sample identification‌ 27.

We further investigate fundamental RL limits‌ and the intersection of learning with privacy. In‌ 6, we formalize the hardness of reinforcement‌ learning with transition look-ahead. In privacy, we establish‌ the optimal best arm identification under differential privacy‌ 13 and provide optimal regret bounds for general‌ bandits under differential privacy constraints 7.

6.4‌ Online algorithms and prophet inequalities

Participants: Matthieu Lerasle‌, Patrick Loiseau, Simon Mauras, Vianney‌ Perchet.

In 15, we revisit prophet‌ inequalities and demonstrate that competing with the top‌ $ℓ$ items is surprisingly easy, establishing vastly improved‌ competitive ratios when the benchmark is relaxed from‌ the single absolute best item. In 10,‌ we investigate online combinatorial allocation with interdependent values,‌ bringing together online secretary-style selection with complex valuation‌ structures to provide the first known approximations for‌ this sequential mechanism design problem.

We also heavily‌ focused on the tradeoffs inherent in learning-augmented algorithms.‌ In 9, we systematically analyze how online‌ algorithms can leverage potentially imperfect machine-learned predictions while‌ maintaining robust worst-case guarantees. We extend this in‌ 8, exploring Pareto-optimality, smoothness, and stochasticity in‌ learning-augmented One-Max-Search problems. Finally, in 24, we‌ establish optimal regret under local search trees for‌ contextual ranking and matching.

6.5 Data valuation, privacy,‌ and causal inference

Participants: Cristina Butucea, Benjamin‌ Heymann, Patrick Loiseau, Maxime Vono,‌ Hugo Richard.

We extensively researched data valuation‌ methodologies to ensure fair credit assignment in machine‌ learning. In 34, we provide an efficient‌ Shapley value approximation via language model arithmetic for‌ LLM fine-tuning data valuation. We further analyze the‌ impact of the utility function itself in semivalue-based‌ data valuation pipelines in 33.

In the‌ realm of privacy and fairness, we present a‌ framework for distribution-aware mean estimation under user-level local‌ differential privacy in 16, improving the statistical‌ utility of private aggregations. We also highlight critical gaps in current AI‌ ethics frameworks, showing in‌ 37 that fairness in‌‌ Generative AI remains understudied and undervalued.

For causal‌ inference and counterfactual reasoning,‌ we introduce CUVET in‌‌ 19, a scalable partitioning approach for continuous‌ treatment assignment. We also‌ developed methods for non-linear‌‌ counterfactual aggregate optimization 29 and counterfactual simulations for‌ large-scale systems with burnout‌ variables 28.

Finally,‌‌ we apply our statistical frameworks to specialized data‌ modalities: solving off-the-grid learning‌ of mixtures from a‌‌ continuous dictionary 22, developing supervised contamination detection‌ for flow cytometry applications‌ 3, and utilizing‌‌ feature-augmented Hawkes processes 36 alongside explainable expected goal‌ metrics 35 for advanced‌ event-level attribution in sports‌‌ analytics.

7 Partnerships and cooperations

7.1 National initiatives‌

Foundry (PEPR IA)

Participants:‌ Patrick Loiseau.

Title:‌‌
Foundry: Foundation of robustness and reliability in AI‌
Partner Institution(s):
- Inria
- CNRS‌
- Université Paris Dauphine
- Institut‌‌ Mines Telecom
- ENS de Lyon
Date/Duration:
2023-2027 (4‌ years)
Additionnal info/keywords:
PEPR‌ IA projet cible, 245k‌‌ euros. Fairness, matching, auctions.

FairPlay (ANR JCJC)

Participants:‌ Patrick Loiseau.

Title:‌
FairPlay: Fair algorithms via‌‌ game theory and sequential learning
Partner Institution(s):
- Inria‌
Date/Duration:
2021-2025 (4 years)‌
Additionnal info/keywords:
ANR JCJC‌‌ project, 245k euros. Fairness, matching, auctions.

DOOM (ANR)‌

Participants: Vianney Perchet.‌

Title:
DOOM: Design of‌‌ Optimal Online Matching Markets
Partner Institution(s):
- Crest, Genes‌
Date/Duration:
2024-2028 (4 years)‌
Additionnal info/keywords:
ANR project,‌‌ 456k euros. online markets, learning, matching.

7.2 Regional‌ initiatives

DOLLY (Hi! Paris)‌

Participants: Vianney Perchet,‌‌ Patrick Loiseau.

Title:
Design, Incentivization, Optimization and‌ (Reinforcement-)Learning of Multi-Layered Market‌
Partner Institution(s):
- Crest, Genes‌‌
- Inria
Date/Duration:
2025-2029 (4 years)
Additionnal info/keywords:
Hi!‌ Paris internal synergy fellowship,‌ 720k euros. online markets,‌‌ learning, matching.

8 Dissemination

8.1 Promoting scientific activities‌

8.1.1 Scientific events: organisation‌

Member of the organizing‌‌ committees

Participants: Vianney Perchet.

Title:
From matchings‌ to markets. A tale‌ of Mathematics, Economics and‌‌ Computer Science.
Partner Institution(s):
- Crest, Genes
Date/Duration:
April‌ 2025
Location:
Cargese

Participants:‌ Vianney Perchet.

Title:‌‌
Member of the scientific advisory committee of the‌ Hi! Paris summer school‌
Partner Institution(s):
Date/Duration:
July‌‌ 2025
Location:
Ecole Polytechnique

Participants: Mariia Vladimirova.‌

Title:
Organizer of the‌ Trustworthy AI symposium labeled‌‌ as AI Action Summit event
Date/Duration:
January 2025‌
Location:
Criteo HQ, PAris‌

8.1.2 Scientific events: selection‌‌

Chair of conference program committees

Participants: Patrick Loiseau‌.

Title:
Program chair‌ of the Hi! Paris‌‌ summer school
Date/Duration:
July 2025
Location:
Ecole Polytechnique‌

Member of the conference‌ program committees

Patrick Loiseau:‌‌
ICML, NeurIPS
Vianney Perchet:
NeurIPS, ICLR, ICML, COLT,‌ ALT, EC
Simon Mauras:‌
WINE, EC
Hugo Richard:‌‌
NeurIPS, ICML, AISTATS
Clément Calauzènes:
ICML, NeurIPS
Benjamin‌ Heymann
AISTATS
Maxime Vono‌
NeurIPS

8.1.3 Invited talks‌‌

Vianney Perchet: Speaker at the French Scientific Summit‌ on Artificial Intelligence (organized‌ by the government at‌‌ Ecole Polytechnique in Feb 2025).
Patrick Loiseau: Keynote‌ speaker at Netgcoop 2025‌
Mariia Vladimirova: Keynote talks‌‌ at Women in Programmatic Network (9 April 2025,‌ Paris, France) and European‌ Women in Tech (26‌‌ June 2025, Amsterdam, Netherlands).‌ Invited talks for Women at Privacy on AI‌ essentials (30 May 2024) and the University of‌ Manchester (19 February 2025, Manchester, Great Britain)
Cristina‌ Butucea: Invited talks (2025) Tsinghua University Beijing, Southeast‌ University Nanjing, China; Symposium on Mathematical Foundations of‌ Trustworthy Machine Learning, Ascona, Switzerland;

8.1.4 Leadership within‌ the scientific community

Vianney Perchet is president of‌ the Scientific Committee, Program Gaspard Monge for Optimisation,‌ Operations Research and Data-Science, Paris-Saclay (2021-2025). Several members‌ of the team are regularly called as experts‌ for their scientific expertise in various selection processes:‌

selection of research grants: Mariia Vladimirova was expert‌ for ANR AAPG projects (2024), Patrick Loiseau was‌ expert for the ANR and the Royal Society‌ (2024), Vianney Perchet, Cristina Butucea and Patrick Loiseau‌ were members of ANR evaluation panels
recruitment committess‌ (many in France, some in Germany), habilitation juries,‌ and tenure committees (Tufts, Hambourg, Harvard)

8.2 Teaching‌ - Supervision - Juries - Educational and pedagogical‌ outreach

8.2.1 Supervision

Patrick Loiseau
: PhD students:‌ Mathieu Molina, Reda Jalal, Minrui Xu, Marie Generali,‌ Melissa Tamine, Louise Allain; postdocs: Simon Finster, Denis‌ Sokolov
Vianney Perchet
: PhD students: Come Fiegel,‌ Maria Cherifa, Mathieu Molina, Ziyad Benomar, Mike Liu,‌ Hafedh El Ferchichi, Matilde Tullii, Giovanni Montanari, Naila‌ Sebastian Esandi. postdocs: Bartholomé Vieille, Achraf Azize, Junghun‌ Kim
Matthieu Lerasle
PhD Students: Clara Carlier, Hugo‌ Chardon, Hafedh El Ferchichi.
Cristina Butucea
PhD students:‌ Nayel Bettache, Henning Stein, Antoine Schoonaert
Marc Abeille‌
: PhD student: Ahmed Ben Yahmed
Clément Calauzènes‌
: PhD student: Morgane Goibert, Maria Cherifa
Benjamin‌ Heymann
: PhD student: Mélissa Tamine
Maxime Vono‌
: PhD student: Mélissa Tamine
Simon Mauras
:‌ PhD student: Minrui Xu, Reda Jalal.

8.2.2 Teaching‌

ENSAE:
- Algorithm design and analysis
  3A lectures
- Programming‌ projet
  1A
- Advanced Optimization
  Third year, lectures
- Theoretical‌ Foundations of Machine Learning
  Second year, lectures
- Stopping‌ time and online algorithms
  Third year, lectures
- Statistics‌
  (ML) 1st and second year
- Nonparametric Statistics
  3rd‌ year, M2
- Mathematical Foundations of Probabilities
  1st year‌
Ecole Polytechnique:
- INF421: design and analysis of algorithms‌
  (Patrick Loiseau). Second-year level, PCs.
- INF581: Advanced Machine‌ Learning and Autonomous Agents
  (Patrick Loiseau). Third-year/M1 level,‌ lectures and labs.
- MAP433: Statistics
  (ML). First-year cycle‌ polytechnicien, PCs.
- MAP576: Learning Theory
  (ML). Second-year cycle‌ polytechnicien, Lecture.
Université Paris-Saclay:
- High Dimensional Probability
  (ML).‌ Master 2
- Stopping Time and Random Algorithm
  (ML).‌ Master 2
PSL:
- Introduction to machine learning
  (Hugo‌ Richard). L3 level, Lectures and labs.
Master IASD:‌
- Recommender Systems
  (Clément Calauzènes). Master 2, Lectures.

8.3‌ Popularization

8.3.1 Specific official responsibilities in science outreach‌ structures

In March 2025, Simon Mauras joined Inria‌ Saclay’s scientific outreach team as the new scientific‌ referent of the research center. In this capacity,‌ Simon participates in the animation of external events‌ (CaféIA, Fête de la Science) and school outreach‌ activities (school visits with the “Chiche!” program).

8.3.2‌ Productions (articles, videos, podcasts, serious games, ...)

Beyond‌ actions in scientific mediation, our team is strongly‌ committed to the scientific dissemination of our research, and interacts in a‌ regular basis with the‌ communication team to produce‌‌ resources aimed at the general public:

8.3.3 Others‌‌ science outreach relevant activities

Simon Mauras and Patrick‌ Loiseau also participated in‌ the Inria Saclay program‌‌ to provide internships for students in “3eme” (middle‌ school) and “2nde” (high‌ school). They created activities‌‌ based on matching problems motivated by Parcoursup to‌ show these young students‌ what science looks like‌‌ and what impact it can have in practice.‌

9 Scientific production

9.1‌ Publications of the year‌‌

International journals

1 articleR.Rémi Castera,‌ P.Patrick Loiseau and‌ B.Bary Pradelski.‌‌ Correlation of Rankings in Matching Markets.Management‌ Science2025. In‌ press. HAL DOI back‌‌ to text
2 articleS.Simon Finster,‌ P. W.Paul W‌ Goldberg and E.Edwin‌‌ Lock. Competitive and Revenue-Optimal Pricing with Budgets‌.Theoretical Economics2025‌. In press. HAL‌‌back to text
3 articleS.Solenne Gaucher‌, G.Gilles Blanchard‌ and F.Frédéric Chazal‌‌. Supervised Contamination Detection, with Flow Cytometry Application‌.Biometrika1124‌August 2025HAL DOI‌‌back to text
4 articleJ.-H.Jung-Hun Kim‌ and S.-Y.Se-Young Yun‌. Adversarial Bandits Against‌‌ Arbitrary Strategies.Transactions on Machine Learning Research‌ JournalOctober 2025.‌ In press. HAL back‌‌ to text
5 articleS.Simon Mauras,‌ P.Paweł Prałat and‌ A.Adrian Vetta.‌‌ A direct proof of the short-side advantage in‌ random matching markets.‌Games and Economic Behavior‌‌154September 2025, 53-61HAL DOI back‌ to text
6 article‌C.Corentin Pla,‌‌ H.Hugo Richard, M.Marc Abeille,‌ N.Nadav Merlis and‌ V.Vianney Perchet.‌‌ On the hardness of Reinforcement Learning with Transition‌ Look-Ahead.Proceedings of‌ Machine Learning Research238‌‌2026HAL DOI back to text

International peer-reviewed‌ conferences

7 inproceedingsA.‌Achraf Azize, Y.‌‌Yulian Wu, J.Junya Honda, F.‌Francesco Orabona, S.‌Shinji Ito and D.‌‌Debabrota Basu. Optimal Regret of Bandits under‌ Differential Privacy.NeurIPS‌ 2025 - 39th Annual‌‌ Conference on Neural Information Processing SystemsSan Diego‌ (USA), United States2025‌HAL back to text‌‌
8 inproceedingsZ.Ziyad Benomar, L.Lorenzo‌ Croissant, V.Vianney‌ Perchet and S.Spyros‌‌ Angelopoulos. Pareto-Optimality, Smoothness, and Stochasticity in Learning-Augmented‌ One-Max-Search.ICML 2025‌ - 42nd International Conference‌‌ on Machine LearningVancouver, CanadaJuly 2025HAL‌back to text
9‌ inproceedingsZ.Ziyad Benomar‌‌ and V.Vianney Perchet. On Tradeoffs in‌ Learning-Augmented Algorithms.AISTATS‌ 2025 - The 28th‌‌ International Conference on Artificial Intelligence and StatisticsPhuket,‌ ThailandJanuary 2025HAL‌back to text
10‌‌ inproceedingsM.Michal Feldman‌, S.Simon Mauras, D.Divyarthi Mohan‌ and R.Rebecca Reiffenhäuser. Online Combinatorial Allocation‌ with Interdependent Values.EC 2025 - 26th‌ ACM Conference on Economics and ComputationStanford, United‌ StatesACMJuly 2025, 189-205HAL DOI‌back to text
11 inproceedingsC.Côme Fiegel‌, P.Pierre Menard, T.Tadashi Kozuno‌, M.Michal Valko and V.Vianney Perchet‌. The Harder Path: Last Iterate Convergence for‌ Uncoupled Learning in Zero-Sum Games with Bandit Feedback‌.Proceedings of Machine Learning ResearchICML 2025‌ - 42nd International Conference on Machine Learning267‌Vancouver, Canada2025HALback to text
12‌ inproceedingsS.Simon Finster. Selling Multiple Complements‌ with Packaging Costs.EC 2025 - 26th‌ ACM Conference on Economics and ComputationStanford (CA),‌ United StatesACMJuly 2025, 250-250HAL‌DOI back to text
13 inproceedingsM.Marc‌ Jourdan and A.Achraf Azize. Optimal Best‌ Arm Identification under Differential Privacy.NeurIPS 2025‌ - 39thh Annual Conference on Neural Information Processing‌ SystemsSan Diego, United StatesOctober 2025HAL‌back to text
14 inproceedingsJ.-H.Jung-Hun Kim‌, M.Milan Vojnović and M.-H.Min-Hwan Oh‌. Oracle-Efficient Combinatorial Semi-Bandits.The Thirty-Ninth Annual‌ Conference on Neural Information Processing SystemsSan Diego,‌ United StatesDecember 2025HAL back to text‌
15 inproceedingsM.Mathieu Molina, N.Nicolas‌ Gast, P.Patrick Loiseau and V.Vianney‌ Perchet. Prophet Inequalities: Competing with the Top‌ Items is Easy.SODA 2025 - ACM-SIAM‌ Symposium on Discrete AlgorithmsNew Orleans, United States‌ACM2025, 1-45HAL back to text‌
16 inproceedingsC.Corentin Pla, M.Maxime‌ Vono and H.Hugo Richard. Distribution-Aware Mean‌ Estimation under User-level Local Differential Privacy.Proceedings‌ of Machine Learning ResearchAISTATS 2025 - 28th‌ International Conference on Artificial Intelligence and Statistics258‌Phuket, ThailandMay 2025HAL back to text‌
17 inproceedingsM.Marius Potfer and V.Vianney‌ Perchet. Comparing Uniform Price and Discriminatory Multi-Unit‌ Auctions through Regret Minimization.NeurIPS 2025 -‌ 39th Annual Conference on Neural Information Processing Systems‌San Diego (California), United StatesDecember 2025HAL‌back to text
18 inproceedingsJ.Joe Suk‌ and J.-H.Jung-Hun Kim. Tracking Most Significant‌ Shifts in Infinite-Armed Bandits.ICML 2025 -‌ 42nd International Conference on Machine LearningVancouver, Canada‌July 2025HAL back to text

Conferences without‌ proceedings

19 inproceedingsA.Artem Betlei, M.‌Mariia Vladimirova, V.Victor Girou and T.‌Thibaud Rahier. CUVET: A Partitioning Approach for‌ Continuous Treatment Assignment At Scale.CauScien 2025‌ - NeurIPS 2025 Workshop: Uncovering Causality in Science‌San-Diego, CA, United StatesDecember 2025HAL back‌ to text
20 inproceedingsB.Benjamin Heymann and‌ M.Marc Lanctot. Learning in Games with‌ progressive hiding.AAMAS 2025Detroit (Michigan), United‌ States2025HAL back to text

Reports &‌ preprints

21 miscA.Ahmed Ben Yahmed, H.Hafedh El Ferchichi‌, M.Marc Abeille‌ and V.Vianney Perchet‌‌. Multi-Armed Bandits with Minimum Aggregated Revenue Constraints‌.October 2025HAL‌back to text
22‌‌ miscC.Cristina Butucea, J.-F.Jean-François Delmas‌, A.Anne Dutfoy‌ and C.Clément Hardy‌‌. Off-the-grid learning of mixtures from a continuous‌ dictionary.April 2025‌HAL back to text‌‌
23 miscL.Lorenzo Croissant. Linear Bandits‌ beyond Inner Product Spaces,‌ the case of Bandit‌‌ Optimal Transport.February 2025HAL back to‌ text
24 miscH.‌Hafedh El Ferchichi,‌‌ M.Matthieu Lerasle and V.Vianney Perchet.‌ Contextual ranking and matching.‌ Optimal regret under LST‌‌.October 2025HALback to text
25‌ miscN.Nathanaël Enriquez‌, M.Mike Liu‌‌, L.Laurent Ménard and V.Vianney Perchet‌. Optimal unimodular matchings‌.March 2025HAL‌‌back to text
26 miscF.Felipe Garrido-Lucero‌, D.Denis Sokolov‌, P.Patrick Loiseau‌‌ and S.Simon Mauras. Two-Sided Matching with‌ Resource-Regional Caps.September‌ 2025HAL back to‌‌ text
27 miscA.Alexandre Gilotte, O.‌Otmane Sakhi, I.‌Imad Aouali and B.‌‌Benjamin Heymann. Offline Contextual Bandit with Counterfactual‌ Sample Identification.September‌ 2025HAL back to‌‌ text
28 miscB.Benjamin Heymann. Counterfactual‌ simulations for large scale‌ systems with burnout variables‌‌.September 2025HALback to text
29‌ miscB.Benjamin Heymann‌ and O.Otmane Sakhi‌‌. Non-Linear Counterfactual Aggregate Optimization.September 2025‌HAL back to text‌
30 miscB.Benjamin‌‌ Heymann. Side-by-side first-price auctions with imperfect bidders‌.December 2025HAL‌back to text
31‌‌ miscM.Mehdi Sebbar, C.Corentin Odic‌, M.Mathieu Léchine‌, A.Aloïs Bissuel‌‌, N.Nicolas Chrysanthos, A.Anthony d'Amato‌, A.Alexandre Gilotte‌, F.Fabian Höring‌‌, S.Sarah Nogueira and M.Maxime Vono‌. CriteoPrivateAds: A Real-World‌ Bidding Dataset to Design‌‌ Private Advertising Systems.April 2025HAL back‌ to text
32 misc‌D.Denis Sokolov.‌‌ College Admissions in France: Affirmative Action, Overlapping Reserves,‌ and Housing Quotas.‌February 2025HAL back‌‌ to text
33 miscM.Mélissa Tamine,‌ B.Benjamin Heymann,‌ P.Patrick Loiseau and‌‌ M.Maxime Vono. On the Impact of‌ the Utility in Semivalue-based‌ Data Valuation.February‌‌ 2025HAL back to text
34 miscM.‌Mélissa Tamine, O.‌Otmane Sakhi and B.‌‌Benjamin Heymann. Data Valuation for LLM Fine-Tuning:‌ Efficient Shapley Value Approximation‌ via Language Model Arithmetic‌‌.December 2025HALback to text
35‌ miscR.Remi Verronneau‌, B.Bastien Cadiou‌‌ and V.Vianney Perchet. EPS : An‌ Explainable Post-Shot Expected Goal‌ Metric for Evaluating Goalkeepers‌‌ and Attackers.September 2025HAL back to‌ text
36 miscR.‌Remi Verronneau, L.‌‌Lorenzo Croissant, B.Bartholomé Vieille and V.‌Vianney Perchet. The‌ Path to the Goal‌‌ : Feature-augmented Hawkes Processes‌ for Event-levelAttribution.February 2026HAL back to‌ text
37 miscM.Mariia Vladimirova, J.-Y.‌Jean-Yves Franceschi and T.Thibaut Issenhuth. Fairness‌ in Generative AI is Understudied, Underachieved, Undervalued.‌October 2025HAL back to text

9.2 Cited‌ publications

38 unpublished24 CFR § 100.75 -‌ Discriminatory advertisements, statements and notices.., https://www.law.cornell.edu/cfr/text/24/100.75back‌ to text
39 articleE.Emmanuel Abbe.‌ Community detection and stochastic block models: recent developments‌.The Journal of Machine Learning Research18‌12017, 6446--6531back to text
40‌ inproceedingsM.Muhammad Ali, P.Piotr Sapiezynski‌, M.Miranda Bogen, A.Aleksandra Korolova‌, A.Alan Mislove and A.Aaron Rieke‌. Discrimination through optimization: How Facebook's ad delivery‌ can lead to skewed outcomes.CSCW2019‌back to text
41 inproceedingsN.Noga Alon‌, N.Nicolo Cesa-Bianchi, O.Ofer Dekel‌ and T.Tomer Koren. Online learning with‌ feedback graphs: Beyond bandits.Annual Conference on‌ Learning Theory40Microtome Publishing2015back to‌ text
42 articleI.Itai Ashlagi, M.‌Maximilien Burq, P.Patrick Jaillet and V.‌Vahideh Manshadi. On matching and thickness in‌ heterogeneous dynamic markets.Operations Research674‌2019, 927--949back to text
43 misc‌I.Itai Ashlagi, P.Patrick Jaillet and‌ V. H.Vahideh H. Manshadi. Kidney Exchange‌ in Dynamic Sparse Heterogenous Pools.2013back‌ to text
44 inproceedingsP.Pranjal Awasthi and‌ T.Tuomas Sandholm. Online stochastic optimization in‌ the large: Application to kidney exchange.Twenty-First‌ International Joint Conference on Artificial Intelligence2009back‌ to text
45 inproceedingsB.Bahman Bahmani and‌ M.Michael Kapralov. Improved bounds for online‌ stochastic matching.European Symposium on AlgorithmsSpringer‌2010, 170--181back to text
46 inproceedings‌E.E. del Barrio, F.F. Gamboa‌, P.P. Gordaliza and J.-M.J.-M. Loubes‌. Obtaining fairness using optimal transport theory.‌ arXiv:1806.031952018, 1--25back to text
47‌ articleB.Benjamin Birnbaum and C.Claire Mathieu‌. On-line bipartite matching made simple.Acm‌ Sigact News3912008, 80--87back‌ to text
48 articleB.Bela Bollobas and‌ G.Graham Brightwell. The width of random‌ graph orders.The Mathematical Scientist2001‌ 1995back to text
49 miscC.Charles‌ Bordenave, M.Marc Lelarge and J.Justin‌ Salez. Matchings on infinite graphs.2011‌back to text
50 bookA.A. Borodin‌ and R.R. El-Yaniv. Online Computation and‌ Competitive Analysis.Cambridge University Presss1998back‌ to text
51 miscA.Allan Borodin,‌ C.Christodoulos Karavasilis and D.Denis Pankratov.‌ An Experimental Study of Algorithms for Online Bipartite‌ Matching.2018back to text
52 inproceedings‌E.E. Boursier and V.V. Perchet.‌ SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits‌.arXiv:1809.081512018, 1--31back to text
53 inproceedingsE.Etienne‌ Boursier and V.Vianney‌ Perchet. Utility/Privacy Trade-off‌‌ through the lens of Optimal Transport.Proceedings‌ of the Twenty Third‌ International Conference on Artificial‌‌ Intelligence and Statistics108Proceedings of Machine Learning‌ ResearchOnlinePMLRAugust‌ 2020, 591--601back‌‌ to text back to text
54 articleS.‌S. Bubeck and N.‌N. Cesa-Bianchi. Regret‌‌ Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems‌.Machine Learning5‌12012, 1--122‌‌back to text
55 articleS.S. Bubeck‌, V.V. Perchet‌ and P.P. Rigollet‌‌. Bounded regret in stochastic multi-armed bandits.‌Journal of Machine Learning‌ Research: Workshop and Conference‌‌ Proceedings (COLT)302013, 122--134back to‌ text
56 inproceedingsN.‌Niv Buchbinder, K.‌‌Kamal Jain and J. (.Joseph (Seffi) Naor‌. Online Primal-Dual Algorithms‌ for Maximizing Ad-Auctions Revenue‌‌.Berlin, Heidelberg2007, 253--264back to‌ text back to text‌
57 articleC.Cristina‌‌ Butucea, A.Amandine Dubois, M.Martin‌ Kroll, A.Adrien‌ Saumard and others.‌‌ Local differential privacy: Elbow effect in optimal density‌ estimation and adaptation over‌ Besov ellipsoids.Bernoulli‌‌2632020, 1727--1764back to text‌
58 articleC.Cristina‌ Butucea, A.Angelika‌‌ Rohde and L.Lukas Steinberger. Interactive versus‌ non-interactive locally, differentially private‌ estimation: Two elbows for‌‌ the quadratic functional.Annals of Statsto‌ appear2023back to‌ text
59 inproceedingsL.‌‌ E.L Elisa Celis, A.Anay Mehrotra‌ and N. K.Nisheeth‌ K Vishnoi. Toward‌‌ Controlling Discrimination in Online Ad Auctions.ICML‌2019back to text‌back to text
60‌‌ inproceedingsN.N. Cesa-Bianchi, C.C. Gentile‌ and Y.Y. Mansour‌. Regret minimization for‌‌ reserve prices in second-price auctions.Proceedings of‌ SODA 20132013back‌ to text
61 book‌‌N.Nicolò Cesa-Bianchi and G.Gabor Lugosi.‌ Prediction, Learning, and Games‌.Cambridge University Press‌‌2006back to text
62 inproceedingsK.K.‌ Chaudhuri, J.J.‌ Imola and A.A.‌‌ Machanavajjhala. Capacity bounded differential privacy.Advances‌ in Neural Information Processing‌ Systems2019, 3469--3478‌‌back to text
63 miscS.Shuchi Chawla‌ and M.Meena Jagadeesan‌. Fairness in ad‌‌ auctions through inverse proportionality.arXiv:2003.139662020back‌ to text back to‌ text
64 inproceedingsI.‌‌ R.Ilan Reuven Cohen and D.David Wajc‌. Randomized online matching‌ in regular graphs.‌‌Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on‌ Discrete AlgorithmsSIAM2018‌, 960--979back to‌‌ text
65 inproceedingsV.Vincent Conitzer, C.‌Christian Kroer, D.‌Debmalya Panigrahi, O.‌‌Okke Schrijvers, E.Eric Sodomka, N.‌ E.Nicolas E Stier-Moses‌ and C.Chris Wilkens‌‌. Pacing equilibrium in first-price auction markets.‌EC2019back to‌ text
66 inproceedingsV.‌‌Vincent Conitzer, C.Christian Kroer, E.‌Eric Sodomka and N.‌ E.Nicolas E Stier-Moses‌‌. Multiplicative pacing equilibria‌ in auction markets.WINE2018back to‌ text
67 inproceedingsJ.José Correa, P.‌Paul Dütting, F.Felix Fischer and K.‌Kevin Schewior. Prophet inequalities for iid random‌ variables from an unknown distribution.Proceedings of‌ the 2019 ACM Conference on Economics and Computation‌2019, 3--17back to text
68 inproceedings‌R.Rachel Cummings, V.Varun Gupta,‌ D.Dhamma Kimpara and J.Jamie Morgenstern.‌ On the Compatibility of Privacy and Fairness.‌FairUMAP2019back to text
69 miscJ.‌Jeffrey Dastin. Amazon scraps secret AI recruiting‌ tool that showed bias against women.Reuters,‌ https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G2018back to text
70 articleC.‌C. Dwork. Differential privacy.Encyclopedia of‌ Cryptography and Security2011, 338--340back to‌ text
71 inproceedingsC.Cynthia Dwork, M.‌Moritz Hardt, T.Toniann Pitassi, O.‌Omer Reingold and R.Richard Zemel. Fairness‌ through awareness.ITCS2012back to text‌
72 inproceedingsC.Cynthia Dwork, F.Frank‌ McSherry, K.Kobbi Nissim and A.Adam‌ Smith. Calibrating noise to sensitivity in private‌ data analysis.Theory of cryptography conferenceSpringer‌2006, 265--284back to text
73 techreport‌R.R. Eilat, K.K. Eliaz and‌ X.X. Mu. Optimal Privacy-Constrained Mechanisms.‌C.E.P.R. Discussion Papers2019back to text
74‌ inproceedingsV.Vitalii Emelianov, N.Nicolas Gast‌, K. P.Krishna P. Gummadi and P.‌Patrick Loiseau. On the Effect of Positive‌ Discrimination on Multistage Selection Problems in the Presence‌ of Implicit Variance.EC2020back to‌ text
75 miscJ.Jon Feldman, A.‌Aranyak Mehta, V.Vahab Mirrokni and S.‌S. Muthukrishnan. Online Stochastic Matching: Beating 1-1/e‌.2009back to text
76 techreportK.‌Kadija Ferryman and M.Mikaela Pitcan. Fairness‌ in Precision Medicine.Data & Society2018‌back to text
77 bookD.D. Fudenberg‌ and J.J. Tirole. Game Theory.‌MIT press1991back to text
78 article‌E.Evrard Garcelon, V.Vianney Perchet,‌ C.Ciara Pike-Burke and M.Matteo Pirotta.‌ Local Differentially Private Regret Minimization in Reinforcement Learning‌.arXiv preprint arXiv:2010.077782020back to text‌
79 articleN.Nicolas Gast, S.Stratis‌ Ioannidis, P.Patrick Loiseau and B.Benjamin‌ Roussillon. Linear Regression from Strategic Data Sources‌.ACM Transactions on Economics and Computation8‌2May 2020, 10:1--10:24back to text‌
80 inproceedingsN.Nicolas Gast and B.Benny‌ Van Houdt. A Refined Mean Field Approximation‌.SIGMETRICS2017back to text
81 article‌M.Maya Gupta, A.Andrew Cotter,‌ M. M.Mahdi Milani Fard and S.Serena‌ Wang. Proxy fairness.arXiv preprint arXiv:1806.11212‌2018back to text
82 inproceedingsA.Anikó‌ Hannák, C.Claudia Wagner, D.David‌ Garcia, A.Alan Mislove, M.Markus Strohmaier and C.Christo‌ Wilson. Bias in‌ online freelance marketplaces: Evidence‌‌ from taskrabbit and fiverr.CSCW2017back‌ to text
83 inproceedings‌M.Moritz Hardt,‌‌ E.Eric Price and N.Nathan Srebro.‌ Equality of Opportunity in‌ Supervised Learning.NIPS‌‌2016back to textback to text
84‌ inproceedingsC.-J.Chien-Ju Ho‌ and J. W.Jennifer‌‌ Wortman Vaughan. Online task assignment in crowdsourcing‌ markets.Twenty-sixth AAAI‌ conference on artificial intelligence‌‌2012back to textback to text
85‌ inproceedingsC.Christina Ilvento‌, M.Meena Jagadeesan‌‌ and S.Shuchi Chawla. Multi-Category Fairness in‌ Sponsored Search Auctions.‌FAT*2020back to‌‌ text back to text
86 articleK.Krishnamurthy‌ Iyer, R.Ramesh‌ Johari and M.Mukund‌‌ Sundararajan. Mean field equilibria of dynamic auctions‌ with learning.Management‌ Science60122014‌‌, 2949--2970back to text
87 articleP.‌Patrick Jaillet and X.‌Xin Lu. Online‌‌ stochastic matching: New algorithms with better bounds.‌Mathematics of Operations Research‌3932014,‌‌ 624--646back to text
88 articleJ.Julie‌ Josse, N.Nicolas‌ Prost, E.Erwan‌‌ Scornet and G.Gaël Varoquaux. On the‌ consistency of supervised learning‌ with missing values.‌‌arXiv preprint arXiv:1902.069312019back to text
89‌ articleN.Nathan Kallus‌, X.Xiaojie Mao‌‌ and A.Angela Zhou. Assessing algorithmic fairness‌ with unobserved protected class‌ using data combination.‌‌arXiv preprint arXiv:1906.002852019back to text
90‌ inproceedingsC.Chinmay Karande‌, A.Aranyak Mehta‌‌ and P.Pushkar Tripathi. Online bipartite matching‌ with unknown distributions.‌Proceedings of the forty-third‌‌ annual ACM symposium on Theory of computing2011‌, 587--596back to‌ text
91 inproceedingsR.‌‌ M.Richard M Karp, U. V.Umesh‌ V Vazirani and V.‌ V.Vijay V Vazirani‌‌. An optimal algorithm for on-line bipartite matching‌.Proceedings of the‌ twenty-second annual ACM symposium‌‌ on Theory of computing1990, 352--358back‌ to text
92 inproceedings‌N.Niki Kilbertus,‌‌ M. R.Mateo Rojas Carulla, G.Giambattista‌ Parascandolo, M.Moritz‌ Hardt, D.Dominik‌‌ Janzing and B.Bernhard Schölkopf. Avoiding discrimination‌ through causal reasoning.‌Advances in Neural Information‌‌ Processing Systems2017, 656--666back to text‌
93 articleJ. S.‌Joon Sik Kim,‌‌ J.Jiahao Chen and A.Ameet Talwalkar.‌ Model-Agnostic Characterization of Fairness‌ Trade-offs.arXiv preprint‌‌ arXiv:2004.034242020back to text back to text‌
94 bookD.D.E.‌ Knuth. Stable Marriage‌‌ and Its Relation to Other Combinatorial Problems: An‌ Introduction to the Mathematical‌ Analysis of Algorithms.‌‌English translation, (CRM Proceedings and Lecture Notes), American‌ Mathematical Society1996back‌ to text
95 book‌‌V.V. Krishna. Auction Theory.Elsevier‌2009back to text‌
96 inproceedingsM. J.‌‌Matt J Kusner, J.Joshua Loftus,‌ C.Chris Russell and‌ R.Ricardo Silva.‌‌ Counterfactual fairness.Advances‌ in neural information processing systems2017, 4066--4076‌back to text
97 miscM. J.Matt‌ J Kusner and J. R.Joshua R Loftus‌. The long road to fairer algorithms.‌2020back to text
98 articleA.Anja‌ Lambrecht and C.Catherine Tucker. Algorithmic Bias?‌ An Empirical Study of Apparent Gender-Based Discrimination in‌ the Display of STEM Career Ads.Management‌ Science2019back to text back to text‌
99 miscJ.Jeff Larson, S.Surya‌ Mattu, L.Lauren Kirchner and J.Julia‌ Angwin. How We Analyzed the COMPAS Recidivism‌ Algorithm.ProPublica, https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm2016back to text‌
100 inproceedingsL.L. Liu, H.H.‌ Mania and M. I.M. I.| Jordan.‌ Competing Bandits in Matching Markets.arXiv:1906.053632019‌, 1--15back to text
101 articleB.‌B. Maćkowiak and M.M. Wiederholt. Business‌ cycle dynamics under rational inattention.The Review‌ of Economic Studies8242015, 1502--1532‌back to text
102 inproceedingsM.Mohammad Mahdian‌ and Q.Qiqi Yan. Online bipartite matching‌ with random arrivals: an approach based on strongly‌ factor-revealing lps.Proceedings of the forty-third annual‌ ACM symposium on Theory of computing2011,‌ 597--606back to text
103 articleV. H.‌Vahideh H Manshadi, S. O.Shayan Oveis‌ Gharan and A.Amin Saberi. Online stochastic‌ matching: Online actions based on offline statistics.‌Mathematics of Operations Research3742012,‌ 559--573back to textback to text
104‌ bookA.A. Mas-Colell, M.M. Whinston‌ and J.J. Green. Microeconomic Theory.‌Oxford University Press1995back to text
105‌ articleA.Andrew Mastin and P.Patrick Jaillet‌. Greedy online bipartite matching on random graphs‌.arXiv preprint arXiv:1307.25362013back to text‌
106 articleF.F. Matėjka and A.A.‌ McKay. Rational inattention to discrete choices: A‌ new foundation for the multinomial logit model.‌American Economic Review10512015, 272--98‌back to text
107 articleA.Aranyak Mehta‌. Online Matching and Ad Allocation.Found.‌ Trends Theor. Comput. Sci.84October 2013‌, 265–368URL: https://doi.org/10.1561/0400000057DOI back to text‌back to text back to text
108 book‌J.-F.Jean-Francois Mertens, S.Sylvain Sorin and‌ S.Shmuel Zamir. Repeated Games.Econometric‌ Society MonographsCambridge University Press2015back to‌ text
109 articleJ.J. Munkres. Algorithms‌ for the Assignment and Transportation Problems.Journal‌ of the Society for Industrial and Applied Mathematics‌511957, 32--38back to text‌
110 inproceedingsM.Milad Nasr and M. C.‌Michael Carl Tschantz. Bidding Strategies with Gender‌ Nondiscrimination Constraints for Online Ad Auctions.FAT*‌2020back to textback to text
111‌ inproceedingsT.T. Nedelec, M.M. Abeille‌, C.C. Calauzenes, B.B. Heymann‌, V.V. Perchet and N.N. El Karoui. The bidder’s‌ standpoint : a simple‌ way to improve bidding‌‌ strategies in revenue-maximizing auctions.Workshop on Learning‌ in the Presence of‌ Strategic Behavior (EC 2019)‌‌2019back to text
112 inproceedingsT.T.‌ Nedelec, N.N.‌ El Karoui and V.‌‌V. Perchet. Learning to bid in revenue-maximizing‌ auctions.Proceedings of‌ the 36th International Conference‌‌ on Machine Learning (ICML 2019)2019, 4781--4789‌back to text
113‌ bookN.N. Nisan‌‌, T.T. Roughgarden, E.E. Tardos‌ and V.V. Vazirani‌. Algorithmic Game Theory‌‌.New York, NY, USACambridge University Press‌2007back to text‌
114 articleV.V.‌‌ Perchet and M.M. Quincampoix. A differential‌ game on Wasserstein space.‌ Application to weak approachability‌‌ with partial monitoring.Journal of Dynamics and‌ Games62019,‌ 65--85back to text‌‌
115 articleV.V. Perchet and P.P.‌ Rigollet. The multi-armed‌ bandit problem with covariates‌‌.Annals of Statistics412013, 693--721‌back to text
116‌ bookJ.Jonas Peters‌‌, D.Dominik Janzing and B.Bernhard Schölkopf‌. Elements of causal‌ inference.The MIT‌‌ Press2017back to text
117 bookG.‌G. Peyré and M.‌M. Cuturi. Computational‌‌ Optimal Transport.ArXiv:1803.005672018back to text‌
118 inproceedingsB.Bashir‌ Rastegarpanah, M.Mark‌‌ Crovella and K.Krishna Gummadi. Fair Inputs‌ and Fair Outputs: The‌ Incompatibility of Fairness in‌‌ Privacy and Accuracy.FairUMAP2020back to‌ text
119 inproceedingsJ.‌J. Reed and B.‌‌B.C. Pierce. Distance makes the types grow‌ stronger: a calculus for‌ differential privacy.ACM‌‌ Sigplan Notices452010, 157--168back to‌ text
120 articleA.‌Angelika Rohde, L.‌‌Lukas Steinberger and others. Geometrizing rates of‌ convergence under local differential‌ privacy constraints.Annals‌‌ of Statistics4852020, 2646--2670back‌ to text
121 article‌A.Alexey Romanov,‌‌ M.Maria De-Arteaga, H.Hanna Wallach,‌ J.Jennifer Chayes,‌ C.Christian Borgs,‌‌ A.Alexandra Chouldechova, S.Sahin Geyik,‌ K.Krishnaram Kenthapadi,‌ A.Anna Rumshisky and‌‌ A. T.Adam Tauman Kalai. What's in‌ a Name? Reducing Bias‌ in Bios without Access‌‌ to Protected Attributes.arXiv preprint arXiv:1904.052332019‌back to text
122‌ articleP.Piotr Sapiezynski‌‌, A.Avijit Gosh, L.Levi Kaplan‌, A.Alan Mislove‌ and A.Aaron Rieke‌‌. Algorithms that" Don't See Color": Comparing Biases‌ in Lookalike and Special‌ Ad Audiences.arXiv‌‌ preprint arXiv:1912.075792019back to text
123 article‌C.C.A. Sims.‌ Implications of rational inattention‌‌.Journal of monetary Economics5032003‌, 665--690back to‌ text
124 inproceedingsT.‌‌Till Speicher, M.Muhammad Ali, G.‌Giridhari Venkatadri, F.‌ N.Filipe Nunes Ribeiro‌‌, G.George Arvanitakis, F.Fabr\'icio Benevenuto‌, K. P.Krishna‌ P. Gummadi, P.‌‌Patrick Loiseau and A.‌Alan Mislove. On the Potential for Discrimination‌ in Online Targeted Advertising.ACM FAT*2018‌back to text
125 bookC.C. Villani‌. Topics in optimal transportation.58Graduate‌ studies in Mathematics, AMS2003back to text‌
126 inproceedingsJ.J. Weed, V.V.‌ Perchet and P.P. Rigollet. Online learning‌ in repeated auctions.Proceedings of COLT 2016‌2016back to textback to text
127‌ inproceedingsB.Blake Woodworth, S.Suriya Gunasekar‌, M. I.Mesrob I. Ohannessian and N.‌Nathan Srebro. Learning Non-Discriminatory Predictors.COLT‌2017back to text
128 inproceedingsY.Yongkai‌ Wu, L.Lu Zhang and X.Xintao‌ Wu. Counterfactual Fairness: Unidentification, Bound and Algorithm.‌.IJCAI2019, 1438--1444back to text‌
129 inproceedingsY.Yongkai Wu, L.Lu‌ Zhang, X.Xintao Wu and H.Hanghang‌ Tong. Pc-fairness: A unified framework for measuring‌ causality-based fairness.Advances in Neural Information Processing‌ Systems2019, 3404--3414back to text
130‌ inproceedingsM. B.Muhammad Bilal Zafar, I.‌Isabel Valera, M.Manuel Gomez Rodriguez and‌ K. P.Krishna P. Gummadi. Fairness Beyond‌ Disparate Treatment & Disparate Impact: Learning Classification Without‌ Disparate Mistreatment.WWW2017back to text‌
131 inproceedingsM. B.Muhammad Bilal Zafar,‌ I.Isabel Valera, M.Manuel Gomez Rodriguez‌, K. P.Krishna P. Gummadi and A.‌Adrian Weller. From Parity to Preference-based Notions‌ of Fairness in Classification.NIPS2017back‌ to text
132 inproceedingsR.Rich Zemel,‌ Y.Yu Wu, K.Kevin Swersky,‌ T.Toni Pitassi and C.Cynthia Dwork.‌ Learning Fair Representations.ICML2013back to‌ text

FAIRPLAY - 2025

FAIRPLAY - 2025

2025﻿﻿﻿‌Activity reportProject-TeamFAIRPLAY﻿‌​‌

Keywords

Computer Science​‌﻿﻿ and Digital Science

Other Research Topics and​​﻿﻿ Application Domains

1 Team﻿​﻿﻿ members, visitors, external collaborators​‌﻿﻿

Research Scientists

Faculty Members​‌﻿﻿

Post-Doctoral Fellows

PhD Students

Technical Staff

Interns and Apprentices​​﻿﻿

Administrative Assistant

External Collaborator

2​​﻿﻿ Overall objectives

2.1 Broad​​​‌ context

2.1.1 Multi-agent​​​‌ systems

2.1.2 Societal aspects​‌﻿﻿ and ethics

Fairness and Discrimination

Privacy vs. Incentives

2.2﻿​﻿﻿ A large variety of​‌﻿﻿ tools and techniques

Game theory﻿​﻿﻿ and economics

Online and reinforcement﻿﻿﻿‌ learning 54

Online algorithms﻿​​﻿ 50 and theoretical computer​​​‌ science

Optimal transport 125​‌﻿﻿

2.3 General objectives

3​​​‌ Research program

3.1 Theme 1:﻿​﻿﻿ Developing fair mechanisms for​‌﻿﻿ auctions and matching problems​​﻿﻿

3.1.1 Fairness in auction-based​​​‌ systems

3.1.2 Fairness in﻿​​﻿ matching and selection problems​​​‌

3.2 Theme﻿​​﻿ 2: Reconciling, and enforcing​​​‌ privacy with fairness

3.3 Theme﻿﻿﻿‌ 3: Exploiting structure in﻿‌​‌ online algorithms and learning﻿​​﻿ problems

3.3.1 Leveraging structure in​​​‌ online matching

3.3.2 Exploiting﻿﻿﻿‌ side-information in sequential learning﻿‌​‌

4​​​‌ Application domains

4.1 Typical﻿​﻿﻿ problems and use-cases

Auctions​​​‌ 95

Matching 94﻿﻿﻿‌, 109

Ethical notions in​​​‌ those use-cases

4.2 Application areas﻿​​﻿

4.2.1 Online​​​‌ advertisement

4.2.2​​​‌ “Physical matching”

5﻿‌​‌ Highlights of the year﻿​​﻿

5.1 Awards

6﻿﻿﻿‌ New results

6.1 Auctions﻿‌​‌ and mechanism design

6.2 Matching markets﻿﻿﻿‌

6.3 Bandits, reinforcement learning,​​​‌ and games

6.4​​​‌ Online algorithms and prophet﻿​﻿﻿ inequalities

6.5 Data valuation, privacy,​‌﻿﻿ and causal inference

7 Partnerships and﻿​​﻿ cooperations

7.1 National initiatives​​​‌

Foundry (PEPR IA)

FairPlay (ANR JCJC)

DOOM (ANR)​​​‌

7.2 Regional​​​‌ initiatives

DOLLY (Hi! Paris)﻿﻿﻿‌

8 Dissemination﻿​​﻿

8.1 Promoting scientific activities​​​‌

8.1.1 Scientific events: organisation﻿﻿﻿‌

Member of the organizing﻿‌​‌ committees

8.1.2 Scientific events: selection﻿‌​‌

Chair of conference program﻿​​﻿ committees

Member of the conference﻿﻿﻿‌ program committees

8.1.3 Invited talks﻿‌​‌

8.1.4 Leadership within​​​‌ the scientific community

8.2 Teaching​​​‌ - Supervision - Juries﻿​﻿﻿ - Educational and pedagogical​‌﻿﻿ outreach

8.2.1 Supervision

8.2.2 Teaching​​​‌

8.3​​​‌ Popularization

8.3.1 Specific official﻿​﻿﻿ responsibilities in science outreach​‌﻿﻿ structures

8.3.2​‌﻿﻿ Productions (articles, videos, podcasts,​​﻿﻿ serious games, ...)

8.3.3 Others﻿‌​‌ science outreach relevant activities﻿​​﻿

9 Scientific production

9.1﻿﻿﻿‌ Publications of the year﻿‌​‌

International journals

International peer-reviewed​​​‌ conferences

Conferences without​‌﻿﻿ proceedings

Reports &​‌﻿﻿ preprints

2025‌Activity reportProject-TeamFAIRPLAY‌‌

Computer Science‌ and Digital Science

Other Research Topics and Application Domains

1 Team members, visitors, external collaborators‌

Faculty Members‌

Interns and Apprentices

2 Overall objectives

2.1 Broad‌ context

2.1.1 Multi-agent‌ systems

2.1.2 Societal aspects‌ and ethics

2.2 A large variety of‌ tools and techniques

Game theory and economics

Online and reinforcement‌ learning 54

Online algorithms 50 and theoretical computer‌ science

Optimal transport 125‌

3‌ Research program

3.1 Theme 1: Developing fair mechanisms for‌ auctions and matching problems

3.1.1 Fairness in auction-based‌ systems

3.1.2 Fairness in matching and selection problems‌

3.2 Theme 2: Reconciling, and enforcing‌ privacy with fairness

3.3 Theme‌ 3: Exploiting structure in‌‌ online algorithms and learning problems

3.3.1 Leveraging structure in‌ online matching

3.3.2 Exploiting‌ side-information in sequential learning‌‌

4‌ Application domains

4.1 Typical problems and use-cases

Auctions‌ 95

Matching 94‌, 109

Ethical notions in‌ those use-cases

4.2 Application areas

4.2.1 Online‌ advertisement

4.2.2‌ “Physical matching”

5‌‌ Highlights of the year

6‌ New results

6.1 Auctions‌‌ and mechanism design

6.2 Matching markets‌

6.3 Bandits, reinforcement learning,‌ and games

6.4‌ Online algorithms and prophet inequalities

6.5 Data valuation, privacy,‌ and causal inference

7 Partnerships and cooperations

7.1 National initiatives‌

DOOM (ANR)‌

7.2 Regional‌ initiatives

DOLLY (Hi! Paris)‌

8 Dissemination

8.1 Promoting scientific activities‌

8.1.1 Scientific events: organisation‌

Member of the organizing‌‌ committees

8.1.2 Scientific events: selection‌‌

Chair of conference program committees

Member of the conference‌ program committees

8.1.3 Invited talks‌‌

8.1.4 Leadership within‌ the scientific community

8.2 Teaching‌ - Supervision - Juries - Educational and pedagogical‌ outreach

8.2.2 Teaching‌

8.3‌ Popularization

8.3.1 Specific official responsibilities in science outreach‌ structures

8.3.2‌ Productions (articles, videos, podcasts, serious games, ...)

8.3.3 Others‌‌ science outreach relevant activities

9.1‌ Publications of the year‌‌

International peer-reviewed‌ conferences

Conferences without‌ proceedings

Reports &‌ preprints

9.2 Cited‌ publications