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Boi Faltings

Publications associées (179)

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

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

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 ...

2022

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 ...

2022

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Can artificial agents benefit from human conventions? Human societies manage to successfully self-organize and resolve the tragedy of the commons in common-pool resources, in spite of the bleak prediction of non-cooperative game theory. On top of that, rea ...

International Foundation for Autonomous Agents and Multiagent Systems2022

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Several optimization scenarios involve multiple agents that desire to protect the privacy of their preferences. There are distributed algorithms for constraint optimization that provide improved privacy protection through secure multiparty computation. How ...

ACM2022

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AI applications find widespread use in a variety of domains. For further acceptance, mostly when multiple agents interact with the system, we must aim to preserve the privacy of participants information in such applications. Towards this, the Yao’s Million ...

ACM2022

Peer-Prediction in the Presence of Outcome Dependent Lying Incentives

Boi Faltings, Naman Goel, Aris Filos Ratsikas

We derive conditions under which a peer-consistency mechanism can be used to elicit truthful data from non-trusted rational agents when an aggregate statistic of the collected data affects the amount of their incentives to lie. Furthermore, we discuss the ...

2022

Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain

Boi Faltings, Naman Goel, Aris Filos Ratsikas

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 ...

2022

ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation

Boi Faltings, Fei Mi, Xiaoyu Lin

Session-based recommendation has received growing attention recently due to the increasing privacy concern. Despite the recent success of neural session-based recommenders, they are typically developed in an offline manner using a static dataset. However, ...

ACM2022

Budget-Bounded Incentives for Federated Learning

Boi Faltings, Aris Filos Ratsikas, Adam Julian Richardson

We consider federated learning settings with independent, self-interested participants. As all contributions are made privately, participants may be tempted to free-ride and provide redundant or low-quality data while still enjoying the benefits of the FL ...

Springer Nature Switzerland AG 20202022

Game-theoretic Mechanisms for Eliciting Accurate Information

Boi Faltings

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 ...

2022