Publications by Boi Faltings | EPFL Graph Search

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

The proliferation of online news creates a need for filtering interesting articles. Compared to other products, however, recommending news has specific challenges: news preferences are subject to trends, users do not want to see multiple articles with simi ...

Large Language Models (LLMs) are increasingly expected to handle complex decision-making tasks, yet their ability to perform structured resource allocation remains underexplored. Evaluating their reasoning is also difficult due to data contamination and the static nature of existing benchmarks. We present a dual-purpose framework leveraging Participatory Budgeting (PB) both as (i) a practical setting for LLM-based resource allocation and (ii) an adaptive benchmark for evaluating their reasoning capabilities

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 ...

We propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw private artificial data samples and empirica ...

We present a new hybrid algorithm for local search in distributed combinatorial optimization. This method is a mix between classical local search methods in which nodes take decisions based only on local information, and full inference methods that guarantee completeness.

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 ...

The modern web critically depends on aggregation of information from self-interested agents, for example opinion polls, product ratings, or crowdsourcing. We consider a setting where multiple objects (questions, products, tasks) are evaluated by a group of ...

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 ...

In electronic commerce, a satisfying buyer experience is a key competitive element. We show new techniques for better adapting interaction with an electronic catalog system to actual buying behavior. Our model replaces the sequential separation of needs identification and product brokering with a conversion in which both processes occur simultaneously. This conversation supports the buyer in formulating his or her needs, and in deciding which criteria to apply in selecting a product to buy. We have experime

We propose ODPOP, a new distributed algorithm for \textit{open multiagent combinatorial optimization} \cite{Faltings2005c}. The ODOP algorithm explores the same search space as the dynamic programming algorithm DPOP~\cite{Petcu2005} or the AND/OR search algorithm AOBB~\cite{Dechter2003}, but does so in an incremental, best-first fashion suitable for open problems. ODPOP has several advantages over DPOP. First, it uses messages whose size only grows linearly with the treewidth of the problem. Second, by lett

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 ...

Contemporary machine learning faces key limitations, such as a lack of integration with planning, incomprehensible structure, and inability to learn continually. We present initial design for system, Agential AI (AAI), that overcomes these issues. AAI's core is a learning method that models temporal dynamics with guarantees of completeness, minimality, and continual learning. It integrates this with a behavior algorithm that plans on a learned model and encapsulates high-level behavior patterns. Preliminary

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 ...

Biased decision making by machine learning systems is increasingly recognized as an important issue. Recently, techniques have been proposed to learn non-discriminatory classifiers by enforcing constraints in the training phase. Such constraints are either ...

People frequently use the world-wide web to find their most preferred item among a large range of options. We call this task preference-based search. The most common tool for preference-based search on the WWW today obtains users' preferences by asking them to fill in a form. It then returns a list of items that most closely match these preferences. Recently, several researchers have proposed tools for preference-based search that elicit preferences from the critiques a user actively makes on examples shown

Recommender systems need to mirror the complexity of the environment they are applied in. The more we know about what might benefit the user, the more objectives the recommender system has. In addition there may be multiple stakeholders - sellers, buyers, shareholders - in addition to legal and ethical constraints. Simultaneously optimizing for a multitude of objectives, correlated and not correlated, having the same scale or not, has proven difficult so far. We introduce a stochastic multi-gradient descent

The Upper Confidence Bounds (UCB) algorithm is a well-known near-optimal strategy for the stochastic multi-armed bandit problem. Its extensions to trees, such as the Upper Confidence Tree (UCT) algorithm, have resulted in good solutions to the problem of G ...

We present the Personalized News (PEN) recommender systems framework, (Note that this paper is an extended abstract of Garcin2013b), currently in use by a newspaper website to evaluate various algorithms for news recommendations. We briefly describe its sy ...

Combinatorial auctions are widely used to sell resources/items. The challenges in such auctions are multi-fold. We need to ensure that bidders, the strategic agents, bid their valuations truthfully to the auction mechanism. Besides, the agents may desire p ...

This article critically examines the foundational principles of contemporary AI methods, exploring the limitations that hinder its potential. We draw parallels between the modern AI landscape and the 20th-century Modern Synthesis in evolutionary biology, a ...

Boi Faltings

Chat with Graph Search

Personalized News Recommendation with Context Trees

LLMs for Resource Allocation: A Participatory Budgeting Approach to Inferring Preferences

Assistive Recipe Editing through Critiquing

Federated Generative Privacy

A hybrid of inference and local search for distributed combinatorial optimization

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

Incentives for Subjective Evaluations with Private Beliefs

Multi-Step Critiquing User Interface for Recommender Systems

Enriching buyers' experiences: the SmartClient approach

ODPOP: An algorithm for Open/Distributed Constraint Optimization

Differentially Private Multi-Agent Constraint Optimization

Agential AI for Integrated Continual Learning, Deliberative Behavior, and Comprehensible Models

Momentum-based Gradient Methods in Multi-Objective Recommendation

Non-Discriminatory Machine Learning through Convex Fairness Criteria

Evaluating Preference-based Search Tools: a Tale of Two Approaches

Multi-Gradient Descent for Multi-Objective Recommender Systems

DUCT: An upper confidence bound approach to distributed constraint optimization problems

PEN recsys: a Personalized News Recommender Systems Framework (DEMO)

A Truthful, Privacy-Preserving, Approximately Efficient Combinatorial Auction For Single-minded Bidders

On the Parallels Between Evolutionary Theory and the State of AI