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Boi Faltings

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Publications associées (160)

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E[DPOP]: Distributed Constraint Optimization under Stochastic Uncertainty using Collaborative Sampling

Boi Faltings, Thomas Léauté

Many applications that require distributed optimization also include uncertainty about the problem and the optimization criteria themselves. However, current approaches to distributed optimization assume that the problem is entirely known before optimizati ...

2009

Incentives for Truthful Information Elicitation of Continuous Signals

Boi Faltings, Goran Radanovic

We consider settings where a collective intelligence is formed by aggregating information contributed from many independent agents, such as product reviews, community sensing, or opinion polls. We propose a novel mechanism that elicits both private signals ...

2014

AI-driven Prices for Externalities and Sustainability in Production Markets

Boi Faltings, Panayiotis Danassis, Aris Filos Ratsikas

Traditional competitive markets do not account for negative externalities; indirect costs that some participants impose on others, such as the cost of over-appropriating a common-pool resource (which diminishes future stock, and thus harvest, for everyone) ...

Cornell University2023

Symmetric Subgame-Perfect Equilibria in Resource Allocation

Boi Faltings, Ludek Cigler

We analyze symmetric protocols to rationally coordinate on an asymmetric, efficient allocation in an infinitely repeated N-agent, C-resource allocation problems, where the resources are all homogeneous. Bhaskar proposed one way to achieve this in 2-agent, ...

AI Access Foundation2014

Multiversion concurrency control for the generalized search tree

Boi Faltings, Ion Constantinescu

Many read-intensive systems where fast access to data is more important than the rate at which data can change make use of multidimensional index structures, like the generalized search tree (GiST). Although in these systems the indexed data are rarely upd ...

2009

Generating Artificial Data for Private Deep Learning

Boi Faltings, Aleksei Triastcyn

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy for the original dataset. We use generative adversarial networks to draw privacy-preserving artificial data samples and d ...

2019

Privacy-Preserving Multi-agent Constraint Satisfaction

Boi Faltings, Thomas Léauté

Constraint satisfaction has been a very successful paradigm for solving problems such as resource allocation and planning. Many of these problems pose themselves in a context involving multiple agents, and protecting privacy of information among them is of ...

IEEE Computer Society Press2009

Symmetric subgame perfect equilibria in resource allocation

Boi Faltings, Ludek Cigler

We analyze symmetric protocols to rationally coordinate on an asymmetric, efficient allocation in an infinitely repeated N-agent, C-resource allocation problems. (Bhaskar 2000) proposed one way to achieve this in 2-agent, 1-resource allocation games: Agent ...

2012

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

Efficient Allocations in Constant Time: Towards Scalable Solutions in the Era of Large Scale Intelligent Systems

Boi Faltings, Panayiotis Danassis

The next technological revolution will be interwoven to the proliferation of intelligent systems. As we bridge the gap between physical and cyber worlds, we will give rise to large-scale, multi-agent based technologies. A key challenge that cities of the f ...

IOS PRESS2020

A Logical Fallacy-Informed Framework for Argument Generation

Boi Faltings, Robert West, Antoine Bosselut, Debjit Paul, Shaobo Cui, Luca Mouchel

Despite the remarkable performance of large language models (LLMs), they still struggle with generating logically sound arguments, resulting in potential risks such as spreading misinformation. An important factor contributing to LLMs' suboptimal performan ...

Association for Computational Linguistics2025

Protecting Privacy through Distributed Computation in Multi-agent Decision Making

Boi Faltings, Thomas Léauté

As large-scale theft of data from corporate servers is becoming increasingly common, it becomes interesting to examine alternatives to the paradigm of centralizing sensitive data into large databases. Instead, one could use cryptography and distributed com ...

AI Access Foundation2013

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

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Boi Faltings, Aleksei Triastcyn

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing ...

MDPI2022

incentive Mechanism Design for Responsible Data Governance: A Large-Scale Field Experiment

Boi Faltings, Naman Goel

A crucial building block of responsible artificial intelligence is responsible data governance, including data collection. Its importance is also underlined in the latest EU regulations. The data should be of high quality, foremost correct and representati ...

2023

LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation

Boi Faltings, Ljubomir Rokvic

In Federated Learning, it is crucial to handle low-quality, corrupted, or malicious data. However, traditional data valuation methods are not suitable due to privacy concerns. To address this, we propose a simple yet effective approach that utilizes a new ...

Institute of Electrical and Electronics Engineers Inc.2024

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

Boi Faltings, Sujit Prakash Gujar

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

Self-training Improves Pre-training for Few-shot Learning in Task-oriented Dialog Systems

Boi Faltings, Fei Mi

As the labeling cost for different modules in task-oriented dialog (ToD) systems is expensive, a major challenge is to train different modules with the least amount of labeled data. Recently, large-scale pre-trained language models, have shown promising re ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Understanding and improving relational matrix factorization in recommender systems

Boi Faltings, Li Pu

Matrix factorization techniques such as the singular value decomposition (SVD) have had great success in recommender systems. We present a new perspective of SVD for constructing a latent space from the training data, which is justified by the theory of hy ...

ACM Press2013

Decentralized Anti-coordination Through Multi-agent Learning

Boi Faltings, Ludek Cigler

To achieve an optimal outcome in many situations, agents need to choose distinct actions from one another. This is the case notably in many resource allocation problems, where a single resource can only be used by one agent at a time. How shall a designer ...

AI Access Foundation2013