Publications by Boi Faltings | EPFL Graph Search

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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 ...

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 ...

Background/Introduction The use of coronary computed tomography angiography (CCTA) in managing high-risk acute coronary syndrome (ACS) patients is increasingly common. Yet, its role remains unvalidated in this context, posing challenges to traditional clinical decision-making algorithms. Physicians, accustomed to a hierarchical and structured approach involving symptom assessment, electrocardiograms, and biomarkers for deciding on the necessity of invasive coronary angiography (ICA), now face dilemmas when

In Federated Learning, heterogeneity in client data distributions often means that a single global model does not have the best performance for individual clients. Consider for example training a next-word prediction model for keyboards: user-specific language patterns due to demographics (dialect, age, etc.), language proficiency, and writing style result in a highly non-IID dataset across clients. Other examples are medical images taken with different machines, or driving data from different vehicle types

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 ...

Local search algorithms have been very successful for solving constraint satisfaction problems (CSP). However, a major weakness has been that local search is unable to detect unsolvability and is thus not suitable for highly constrained or overconstrained problems. In this paper, we present a scheme where a local search algorithm, the breakout algorithm, is used to identify hard or unsolvable subproblems. This is used in two ways. The first to generate a fail-first variable order for a systematic backtrack

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 ...

We present in this paper an approximative method for distributed combinatorial optimization problems based on dynamic programming. The algorithm is a utility propagation method and requires a linear number of messages. The largest message is in the worst case exponential in the width of the constraint graph, in case of exact computation. However, it can be tuned with 2 parameters that allow the user to specify the desired tradeoff between solution quality and computational complexity. The second part of thi

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 ...

Supporting recommendations with personalized and relevant explanations increases trust and perceived quality, and helps users make better decisions. Prior work attempted to generate a synthetic review or review segment as an explanation, but they were not judged convincing in evaluations by human users. We propose T-RECS, a multi-task learning Transformer-based model that jointly performs recommendation with textual explanations using a novel multi-aspect masking technique. We show that human users signific

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

Current web infrastructure is oriented on human-machine interactions. Developments for next generation large-scale information systems (such as Web Services and ebXML) however aim to allow for automated interactions between arbitrary systems / services. This is achieved by supplying high level descriptions of service capabilities and enabling information systems to dynamically discover and access each other. Fundamental to progress towards this vision is the understanding of the behavioural descriptions tha

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 ...

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 ...

Defeasibility in causal reasoning implies that the causal relationship between cause and effect can be strengthened or weakened. Namely, the causal strength between cause and effect should increase or decrease with the incorporation of strengthening argume ...

Boi Faltings

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Privacy-Preserving Multi-agent Constraint Satisfaction

Symmetric subgame perfect equilibria in resource allocation

Assessing the need for coronary angiography in high-risk acute coronary syndrome patients using artificial intelligence and computed tomography

Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data

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

Using the Breakout Algorithm to Identify Hard and Unsolvable Subproblems

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

Approximations in Distributed Optimization

Protecting Privacy through Distributed Computation in Multi-agent Decision Making

T-RECS: a Transformer-based Recommender Generating Textual Explanations and Integrating Unsupervised Language-based Critiquing

Incentives for Effort in Crowdsourcing using the Peer Truth Serum

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

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

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

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

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

Behavioural Description Formalisms for Service Integration - Survey and Comparation

Decentralized Anti-coordination Through Multi-agent Learning

Understanding and improving relational matrix factorization in recommender systems

Exploring Defeasibility in Causal Reasoning