Concept
Algorithmic mechanism design
Publications associées (32)
Extensions of Peer Prediction Incentive Mechanisms
As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...
EPFL2024Game-theoretic Mechanisms for Eliciting Accurate Information
Artificial Intelligence often relies on information obtained from others through crowdsourcing, federated learning, or data markets. It is crucial to ensure that this data is accurate. Over the past 20 years, a variety of incentive mechanisms have been dev ...
2022Tackling Peer-to-Peer Discrimination in the Sharing Economy
Boi Faltings, Naman Goel, Maxime Rutagarama
Sharing economy platforms such as Airbnb and Uber face a major challenge in the form of peer-to-peer discrimination based on sensitive personal attributes such as race and gender. As shown by a recent study under controlled settings, reputation systems can ...
ACM2020Functional Inverse Problems on Spheres: Theory, Algorithms and Applications
Matthieu Martin Jean-André Simeoni
Many scientific inquiries in natural sciences involve approximating a spherical field -namely a scalar quantity defined over a continuum of directions- from generalised samples of the latter (e.g. directional samples, local averages, etc). Such an approxim ...
EPFL2020Deep Bayesian Trust : A Dominant and Fair Incentive Mechanism for Crowd
An important class of game-theoretic incentive mechanisms for eliciting effort from a crowd are the peer based mechanisms, in which workers are paid by matching their answers with one another. The other classic mechanism is to have the workers solve some g ...
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019Mirrored Langevin Dynamics
Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis
We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...
2018Mirrored Langevin Dynamics
Volkan Cevher, Paul Thierry Yves Rolland, Ya-Ping Hsieh, Ali Kavis
We consider the problem of sampling from constrained distributions, which has posed significant challenges to both non-asymptotic analysis and algorithmic design. We propose a unified framework, which is inspired by the classical mirror descent, to derive ...
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018Learning embeddings: efficient algorithms and applications
Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...
EPFL2018Learning embeddings: efficient algorithms and applications
Learning to embed data into a space where similar points are together and dissimilar points are far apart is a challenging machine learning problem. In this dissertation we study two learning scenarios that arise in the context of learning embeddings and o ...
École Polytechnique Fédérale de Lausanne2018