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Boi Faltings

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Related publications (166)

Deep 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 ...

AAAI2019

Context-Tree Recommendation vs Matrix-Factorization: Algorithm Selection and Live Users Evaluation

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

We describe the selection, implementation and online evaluation of two e-commerce recommender systems developed with our partner company, Prediggo. The first one is based on the novel method of Bayesian Variable-order Markov Modeling (BVMM). The second, SS ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2019

A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders

Boi Faltings, Sujit Prakash Gujar, Sankarshan Damle

Combinatorial auctions are widely used to sell resources/items. The challenges in such auctions are multi-fold. We need to ensure that bidders, the strategic agents, bid their valuations truthfully to the auction mechanism. Besides, the agents may desire p ...

ASSOC COMPUTING MACHINERY2019

Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization

Boi Faltings, Diego Matteo Antognini

Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on h ...

2019

Federated Learning with Bayesian Differential Privacy

Boi Faltings, Aleksei Triastcyn

We consider the problem of reinforcing federated learning with formal privacy guarantees. We propose to employ Bayesian differential privacy, a relaxation of differential privacy for similarly distributed data, to provide sharper privacy loss bounds. We ad ...

IEEE2019

A Bayesian Approach to Intervention-Based Clustering

Boi Faltings, Igor Kulev

2018

Partial Truthfulness in Minimal Peer Prediction Mechanisms with Limited Knowledge

Boi Faltings, Goran Radanovic

We study minimal single-task peer prediction mechanisms that have limited knowledge about agents' beliefs. Without knowing what agents' beliefs are or eliciting additional information, it is not possible to design a truthful mechanism in a Bayesian-Nash se ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Privacy Preserving and Cost Optimal Mobile Crowdsensing using Smart Contracts on Blockchain

Boi Faltings, Sujit Prakash Gujar, Dimitrios Chatzopoulos

The popularity and applicability of mobile crowdsensing applications are continuously increasing due to the widespread of mobile devices and their sensing and processing capabilities. However, we need to offer appropriate incentives to the mobile users who ...

IEEE2018

Non-Discriminatory Machine Learning through Convex Fairness Criteria

Boi Faltings, Naman Goel, Mohammad Yaghini

Biased decision making by machine learning systems is increasingly recognized as an important issue. Recently, techniques have been proposed to learn non-discriminatory classifiers by enforcing constraints in the training phase. Such constraints are either ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Information Gathering with Peers: Submodular Optimization with Peer-Prediction Constraints

Boi Faltings, Goran Radanovic, Andreas Krause

We study a problem of optimal information gathering from multiple data providers that need to be incentivized to provide accurate information. This problem arises in many real world applications that rely on crowdsourced data sets, but where the process of ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2018

Auction Based Mechanisms for Dynamic Task Assignments in Expert Crowdsourcing

Boi Faltings, Sujit Prakash Gujar

Crowdsourcing marketplaces link large populations of workers to an even larger number of tasks. Thus, it is necessary to have mechanisms for matching workers with interesting and suitable tasks. Earlier work has addressed the problem of finding optimal wor ...

Springer International Publishing Ag2017

DUCT: An Upper Confidence Bound Approach to Distributed Constraint Optimization Problems

Boi Faltings, Christos Dimitrakakis, Brammert Ottens

We propose a distributed upper confidence bound approach, DUCT, for solving distributed constraint optimization problems. We compare four variants of this approach with a baseline random sampling algorithm, as well as other complete and incomplete algorith ...

Assoc Computing Machinery2017

Mechanismen zur Beschaffung korrekter Daten

Boi Faltings, Goran Radanovic

"Smart Cities" brauchen als Grundlage für Entscheidungen grosse Mengen von Daten, die häufig nicht direkt, sondern über andere Agenten erhoben werden. Es ist daher wichtig, die Qualität durch geeignete Anreize oder Kontrolle sicherzustellen. Bei Ausnutzung ...

2016

Poster: LocalCoin: An Ad-hoc Payment Scheme for Areas with High Connectivity

Boi Faltings, Sujit Prakash Gujar, Dimitrios Chatzopoulos

The popularity of digital currencies, especially cryptocurrencies, has been continuously growing since the appearance of Bitcoin. Bitcoin is a peer-to-peer (P2P) cryptocurrency protocol enabling transactions between individuals without the need of a truste ...

Assoc Computing Machinery2016

Recommender system for an online multimedia content provider

Boi Faltings, Florent Frédéric Garcin

A recommender system for determining multimedia items to be presented in an online interface of a multimedia content provider. The recommender system continuously observes the user's behavior in order to define a context for the user which on the basis of ...

2016

Incentives for Effort in Crowdsourcing using the Peer Truth Serum

Boi Faltings, Radu Jurca, Goran Radanovic

Crowdsourcing is widely proposed as a method to solve large variety of judgement tasks, such as classifying website content, peer grading in online courses, or collecting real-world data. As the data reported by workers cannot be verified, there is a tende ...

Assoc Computing Machinery2016

Limiting the Influence of Low Quality Information in Community Sensing

Boi Faltings, Goran Radanovic

We consider a community of private sensors that collect measurements of a physical phenomenon, such as air pollution, and report it to a center. The center should be able to prevent low quality reports from degrading the quality of the aggregated informati ...

2016

Online Auctions for Dynamic Assignment: Theory and Empirical Evaluation

Boi Faltings, Sujit Prakash Gujar

Dynamic resource assignment is a common problem in multi-agent systems. We consider scenarios in which dynamic agents have preferences about assignments and the resources that can be assigned using online auctions. We study the trade-off between the follow ...

Ios Press2016

Learning to Scale Payments in Crowdsourcing with PropeRBoost

Boi Faltings, Goran Radanovic

Motivating workers to provide significant effort has been recognized as an important issue in crowdsourcing. It is important not only to compensate worker effort, but also to discourage low-quality workers from participating. Several proper incentive schem ...

2016

Personalizing Product Rankings Using Collaborative Filtering on Opinion-Derived Topic Profiles

Boi Faltings, Claudiu-Cristian Musat

Product review sites such as TripAdvisor, Yelp or Amazon provide a single, non personalized ranking of products. The sparse review data makes personalizing recommendations difficult. Topic Profile Collaborative Filtering exploits review texts to identify u ...

2015

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