Publications by Boi Faltings | EPFL Graph Search

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Prediction markets and other reward mechanisms based on proper scoring rules can elicit accurate predictions about future events with public utcomes. However, many questions of public interest do not always have a clear answer. For example, facts such as t ...

Federated Learning by nature is susceptible to low-quality, corrupted, or even malicious data that can severely degrade the quality of the learned model. Traditional techniques for data valuation cannot be applied as the data is never revealed. We present ...

Providing explanations for recommended items allows users to refine the recommendations by critiquing parts of the explanations. As a result of revisiting critiquing from the perspective of multimodal generative models, recent work has proposed M&Ms-VAE, w ...

We define the distributed continuous-time combinatorial optimization problem. We propose a new notion of solution stability in aynamic optimization, based on the cost Of change from an already-implemented solution to the new one. Change costs are modeled with stability constraints, and can evolve over time.

%0 Conference Paper %T Bayesian Differential Privacy for Machine Learning %A Aleksei Triastcyn %A Boi Faltings %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E A ...

The problems associated with computerized search tools in online product search environments are discussed. The application of the Utility Theory, using a tool for the travel planning domain is illustrated. The features of Isy-Travel, a tool based on Smart Client Technology, are also discussed. The tool allows users to search for a flight itinerary according to their personal preferences and constraints.

The object of the present invention is a method that allows a group of independent participants to coordinate decisions with respect to the allocation of interdependent resources, while maintaining certain privacy guarantees. The present invention proposes ...

A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re- main unclear. Previous works only compare decoding algorithms in narrow sc ...

Travel has many situations where context-aware computing can bring important benefits. In this paper, we describe an approach for integrating context-aware computing to a mobile travel assistant. Travel plans, generated using reality [2], are enriched within compact and powerful structures, called User Task Models. These structures are transferred to a mobile device enabling the support for the traveler during his trip.

A recommender system comprising at least one computing device which comprises one or more processors and one or more computer-readable storage media operatively coupled to at least one of the processors, wherein said computing device performs a method of a ...

Crafting an appealing heading is crucial for attracting readers and marketing work or products. A popular way is to summarize the main idea with a refined description and a memorable acronym. However, there lacks a systematic study and a formal benchmark i ...

Using personalized explanations to support recommendations has been shown to increase trust and perceived quality. However, to actually obtain better recommendations, there needs to be a means for users to modify the recommendation criteria by interacting ...

Natural language generation (NLG) is an essential component of task-oriented dialog systems. Despite the recent success of neural approaches for NLG, they are typically developed in an offline manner for particular domains. To better fit real-life applicat ...

We present a multi-agent learning algorithm, ALMA-Learning, for efficient and fair allocations in large-scale systems. We circumvent the traditional pitfalls of multi-agent learning (e.g., the moving target problem, the curse of dimensionality, or the need ...

Method of detecting anomalous behaviour in a computer network comprising the steps of - monitoring network traffic flowing in a computer network system, - authenticating users to which network packets of the network traffic are associated, - extracting par ...

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 ...

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 ...

Blockchain based systems allow various kinds of financial transactions to be executed in a decentralized manner. However, these systems often rely on a trusted third party (oracle) to get correct information about the real-world events, which trigger the f ...

Boi Faltings

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Incentives for Answering Hypothetical Questions

A Practical Influence Approximation for Privacy-Preserving Data Filtering in Federated Learning

Positive and Negative Critiquing for VAE-based Recommenders

Optimal solution stability in dynamic, distributed constraint optimization

Bayesian Differential Privacy for Machine Learning

Effective interaction principles for online product search environments

Method to allocate inter-dependent resources by a set of participants

Language Model Decoding as Likelihood–Utility Alignment

Towards an Intelligent Mobile Travel Assistant

Recommendation Strategy Portfolios

Unveiling the Art of Heading Design: A Harmonious Blend of Summarization, Neology, and Algorithm

Interacting with Explanations through Critiquing

Continual Learning for Natural Language Generation in Task-oriented Dialog Systems

Improving Multi-agent Coordination by Learning to Estimate Contention

Method of detecting anomalous behaviour in a computer network

Continual Learning for Natural Language Generation in Task-oriented Dialog Systems

Heterogeneous Attribute Utility Model: A new approach for modeling user profiles for recommendation systems

Limiting the Influence of Low Quality Information in Community Sensing

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

Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain