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The Vehicle Routing Problem (VRP) has been extensively studied over the last twenty years, because it is an abstraction of many real-life logistics problems. In its multiple-depot variant (MDVRP), the routes of vehicles located at various depots must be op ...

Online reviews have become increasingly popular as a way to judge the quality of various products and services. However, recent work demonstrates that the absence of reporting incentives leads to a biased set of reviews that may not reflect the true qualit ...

Automated predictions require explanations to be interpretable by humans. Past work used attention and rationale mechanisms to find words that predict the target variable of a document. Often though, they result in a tradeoff between noisy explanations or ...

Preference-based search is the problem of finding an item that matches best with a user's preferences. User studies show that example-based tools for preference-based search can achieve significantly higher accuracy when they are complemented with suggesti ...

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 ...

The goal of fairness in classification is to learn a classifier that does not discriminate against groups of individuals based on sensitive attributes, such as race and gender. One approach to designing fair algorithms is to use relaxations of fairness not ...

Assessing whether AI-generated images are substantially similar to copyrighted works is a crucial step in resolving copyright disputes. In this paper, we propose CopyJudge, an automated copyright infringement identification framework that leverages large vision-language models (LVLMs) to simulate practical court processes for determining substantial similarity between copyrighted images and those generated by text-to-image diffusion models. Specifically, we employ an abstraction-filtration-comparison test f

We consider schemes for obtaining truthful reports on a common but hidden signal from large groups of rational, self-interested agents. One example are online feedback mechanisms, where users provide observations about the quality of a product or service s ...

Distributed constraint optimization (DCOP) is a framework in which multiple agents with private constraints (or preferences) cooperate to achieve a common goal optimally. DCOPs are applicable in several multi-agent coordination/allocation problems, such as ...

Federated learning (FL) allows machine learning models to be trained on distributed datasets without directly accessing local data. In FL markets, numerous Data Consumers compete to recruit Data Owners for their respective training tasks, but budget constraints and competition can prevent them from securing sufficient data. While existing solutions focus on optimizing one-to-one matching between Data Owners and Data Consumers, we propose \methodname{}, a novel framework that facilitates collaborative recrui

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 ...

We consider a participatory sensing scenario where a group of private sensors observes the same phenomenon, such as air pollution. Since sensors need to be installed and maintained, owners of sensors are inclined to provide inaccurate or random data. We de ...

Today's research progress in the field of multi-document summarization is obstructed by the small number of available datasets. Since the acquisition of reference summaries is costly, existing datasets contain only hundreds of samples at most, resulting in ...

Emoticons are widely used to express positive or negative sentiment on Twitter. We report on a study with live users to determine whether emoticons are used to merely emphasize the sentiment of tweets, or whether they are the main elements carrying the sen ...

In this paper, we develop two methods for improving the performance of the standard Distributed Breakout Algorithm \cite{yokoo:dba} using the notion of interchangeability. We study the performance of this algorithm on the problem of distributed sensor networks. In particular, we consider how neighborhood interchangeability and neighborhood partial interchangeability \cite{freuder:interch} can be used to keep conflicts localized and avoid ``chain reactions'' where a conflict originating in one part of the pr

Many games have undesirable Nash equilibria. For exam- ple consider a resource allocation game in which two players compete for an exclusive access to a single resource. It has three Nash equilibria. The two pure-strategy NE are effi- cient, but not fair. ...

Sharing economy platforms such as Airbnb and Uber face a major challenge in the form of peer-to-peer discrimination based on sensitive personal attributes such as race and gender. As shown by a recent study under controlled settings, reputation systems can ...

We define the distributed, real-time combinatorial optimization problem. We propose a general, semantically well-defined notion of solution stability in such systems, based on the cost of change from an already implemented solution to the new one. This approach allows maximum flexibility in specifying these costs through the use of stability constraints. We present the first mechanism for combinatorial optimization that guarantees optimal solution stability in dynamic environments, based on this notion of s

Continual learning for reinforcement learning agents remains a significant challenge, particularly in preserving and leveraging existing information without an external signal to indicate changes in tasks or environments. In this study, we explore the effectiveness of autoencoders in detecting new tasks and matching observed environments to previously encountered ones. Our approach integrates policy optimization with familiarity autoencoders within an end-to-end continual learning system. This system can re

It has been widely recognised that matchmaking is an important component of heterogeneous multiagent systems. Several researchers have developed powerful techniques for the matchmaking problem in general. There are also specific representation of agent capabilities such as DAML-S which provide a more specific framework for matchmaking. Most approaches to matchmaking have assumed a sequential search for an agent with matching capabilities. This may become intractable when the number of available agents gets

Boi Faltings

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Ensuring Privacy through Distributed Computation in Multiple-Depot Vehicle Routing Problems

Reporting incentives and biases in online review forums

Multi-Dimensional Explanation of Target Variables from Documents

The lookahead principle for preference elicitation: Experimental results

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

Addressing fairness in classification with a model-agnostic multi-objective algorithm

CopyJudge: Automated Copyright Infringement Identification and Mitigation in Text-to-Image Diffusion Models

Mechanisms for Making Crowds Truthful

Differentially private multi-agent constraint optimization

FedCDC: A Collaborative Framework for Data Consumers in Federated Learning Market

Fast Multi-Step Critiquing for VAE-based Recommender Systems

Incentive Schemes for Participatory Sensing

GameWikiSum: a Novel Large Multi-Document Summarization Dataset

A :) Is Worth a Thousand Words: How People Attach Sentiment to Emoticons and Words in Tweets

Applying interchangeability techniques to the distributed breakout algorithm

Reaching Correlated Equilibria Through Multi-agent Learning

Tackling Peer-to-Peer Discrimination in the Sharing Economy

Optimal Solution Stability in Real Time Optimization

Continual Reinforcement Learning via Autoencoder-Driven Task and New Environment Recognition

Efficient Matchmaking and Directory Services