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Boi Faltings

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Publications associées (166)

Putting ridesharing to the test: efficient and scalable solutions and the power of dynamic vehicle relocation

Boi Faltings, Panayiotis Danassis, Marija Sakota

We study the optimization of large-scale, real-time ridesharing systems and propose a modular design methodology, Component Algorithms for Ridesharing (CAR). We evaluate a diverse set of CARs (14 in total), focusing on the key algorithmic components of rid ...

SPRINGER2022

Introduction to the Special Issue on the Federated Learning: Algorithms, Systems, and Applications: Part 1

Boi Faltings, Qiang Yang, Yang Liu

ASSOC COMPUTING MACHINERY2022

Positive and Negative Critiquing for VAE-based Recommenders

Boi Faltings, Diego Matteo Antognini

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

Exploiting environmental signals to enable policy correlation in large-scale decentralized systems

Boi Faltings, Panayiotis Danassis, Zeki Doruk Erden

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 ...

SPRINGER2022

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

Slim: Explicit Slot-Intent Mapping With Bert For Joint Multi-Intent Detection And Slot Filling

Boi Faltings, Fei Mi, Fengyu Cai, Wanhao Zhou

Utterance-level intent detection and token-level slot filling are two key tasks for spoken language understanding (SLU) in task-oriented systems. Most existing approaches assume that only a single intent exists in an utterance. However, there are often mul ...

IEEE2022

Improved Cooperation by Exploiting a Common Signal

Boi Faltings, Panayiotis Danassis, Zeki Doruk Erden

International Foundation for Autonomous Agents and Multiagent Systems2022

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

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

Assistive Recipe Editing through Critiquing

Boi Faltings, Diego Matteo Antognini

There has recently been growing interest in the automatic generation of cooking recipes that satisfy some form of dietary restrictions, thanks in part to the availability of online recipe data. Prior studies have used pre-trained language models, or relied ...

2022

A Distributed Differentially Private Algorithm for Resource Allocation in Unboundedly Large Settings

Boi Faltings, Aleksei Triastcyn, Panayiotis Danassis

We introduce a practical and scalable algorithm (PALMA) for solving one of the fundamental problems of multi-agent systems -- finding matches and allocations -- in unboundedly large settings (e.g., resource allocation in urban environments, mobility-on-dem ...

2022

Blockchain-based Practical Multi-agent Secure Comparison and its Application in Auctions

Boi Faltings, Sujit Prakash Gujar, Sankarshan Damle

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

Multi-Step Critiquing User Interface for Recommender Systems

Boi Faltings, Diego Matteo Antognini, Diana Andreea Petrescu

Recommendations with personalized explanations have been shown to increase user trust and perceived quality and help users make better decisions. Moreover, such explanations allow users to provide feedback by critiquing them. Several algorithms for recomme ...

ASSOC COMPUTING MACHINERY2021

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

Boi Faltings, Fei Mi, Liangwei Chen, Mengjie Zhao

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 ...

2021

Interacting with Explanations through Critiquing

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

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 ...

2021

Orthos: A Trustworthy AI Framework for Data Acquisition

Boi Faltings, Sujit Prakash Gujar, Dimitrios Chatzopoulos

Information acquisition through crowdsensing with mobile agents is a popular way to collect data, especially in the context of smart cities where the deployment of dedicated data collectors is expensive and ineffective. It requires efficient information el ...

Springer2021

Momentum-based Gradient Methods in Multi-Objective Recommendation

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Multi-objective gradient methods are becoming the standard for solving multi-objective problems. Among others, they show promising results in developing multi-objective recommender systems with both correlated and conflicting objectives. Classic multi-grad ...

2021

Modeling Online Behavior in Recommender Systems: The Importance of Temporal Context

Boi Faltings, Claudiu-Cristian Musat, Diego Matteo Antognini

Simulating online recommender system performance is no- toriously difficult and the discrepancy between the online and offline behaviors is typically not accounted for in offline evaluations. Recommender systems research tends to evalu- ate model performan ...

2021

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Boi Faltings, Diego Matteo Antognini

Recent studies have shown that providing personalized explanations alongside recommendations increases trust and perceived quality. Furthermore, it gives users an opportunity to refine the recommendations by critiquing parts of the explanations. On one han ...

ASSOC COMPUTING MACHINERY2021

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

Boi Faltings, Fei Mi, Lingjing Kong, Fengyu Cai, Wanhao Zhou

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

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