Search Results for author: Dino Pedreschi

Found 18 papers, 6 papers with code

A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions

no code implementations29 Jun 2024 Luca Pappalardo, Emanuele Ferragina, Salvatore Citraro, Giuliano Cornacchia, Mirco Nanni, Giulio Rossetti, Gizem Gezici, Fosca Giannotti, Margherita Lalli, Daniele Gambetta, Giovanni Mauro, Virginia Morini, Valentina Pansanella, Dino Pedreschi

Recommendation systems and assistants (in short, recommenders) are ubiquitous in online platforms and influence most actions of our day-to-day lives, suggesting items or providing solutions based on users' preferences or requests.

Diversity Recommendation Systems

AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems

no code implementations9 Feb 2024 Clara Punzi, Roberto Pellungrini, Mattia Setzu, Fosca Giannotti, Dino Pedreschi

Everyday we increasingly rely on machine learning models to automate and support high-stake tasks and decisions.

Decision Making

Human-AI Coevolution

no code implementations23 Jun 2023 Dino Pedreschi, Luca Pappalardo, Emanuele Ferragina, Ricardo Baeza-Yates, Albert-Laszlo Barabasi, Frank Dignum, Virginia Dignum, Tina Eliassi-Rad, Fosca Giannotti, Janos Kertesz, Alistair Knott, Yannis Ioannidis, Paul Lukowicz, Andrea Passarella, Alex Sandy Pentland, John Shawe-Taylor, Alessandro Vespignani

Human-AI coevolution, defined as a process in which humans and AI algorithms continuously influence each other, increasingly characterises our society, but is understudied in artificial intelligence and complexity science literature.

Recommendation Systems

Dense Hebbian neural networks: a replica symmetric picture of supervised learning

no code implementations25 Nov 2022 Elena Agliari, Linda Albanese, Francesco Alemanno, Andrea Alessandrelli, Adriano Barra, Fosca Giannotti, Daniele Lotito, Dino Pedreschi

We consider dense, associative neural-networks trained by a teacher (i. e., with supervision) and we investigate their computational capabilities analytically, via statistical-mechanics of spin glasses, and numerically, via Monte Carlo simulations.

Retrieval valid

Dense Hebbian neural networks: a replica symmetric picture of unsupervised learning

no code implementations25 Nov 2022 Elena Agliari, Linda Albanese, Francesco Alemanno, Andrea Alessandrelli, Adriano Barra, Fosca Giannotti, Daniele Lotito, Dino Pedreschi

We consider dense, associative neural-networks trained with no supervision and we investigate their computational capabilities analytically, via a statistical-mechanics approach, and numerically, via Monte Carlo simulations.

valid

Benchmarking and Survey of Explanation Methods for Black Box Models

1 code implementation25 Feb 2021 Francesco Bodria, Fosca Giannotti, Riccardo Guidotti, Francesca Naretto, Dino Pedreschi, Salvatore Rinzivillo

The widespread adoption of black-box models in Artificial Intelligence has enhanced the need for explanation methods to reveal how these obscure models reach specific decisions.

Benchmarking

GLocalX -- From Local to Global Explanations of Black Box AI Models

1 code implementation19 Jan 2021 Mattia Setzu, Riccardo Guidotti, Anna Monreale, Franco Turini, Dino Pedreschi, Fosca Giannotti

Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other.

Decision Making

Predicting seasonal influenza using supermarket retail records

1 code implementation8 Dec 2020 Ioanna Miliou, Xinyue Xiong, Salvatore Rinzivillo, Qian Zhang, Giulio Rossetti, Fosca Giannotti, Dino Pedreschi, Alessandro Vespignani

In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting.

FairLens: Auditing Black-box Clinical Decision Support Systems

no code implementations8 Nov 2020 Cecilia Panigutti, Alan Perotti, Andrè Panisson, Paolo Bajardi, Dino Pedreschi

The pervasive application of algorithmic decision-making is raising concerns on the risk of unintended bias in AI systems deployed in critical settings such as healthcare.

Decision Making Explainable artificial intelligence +2

Clusters of investors around Initial Public Offering

no code implementations31 May 2019 Margarita Baltakienė, Kęstutis Baltakys, Juho Kanniainen, Dino Pedreschi, Fabrizio Lillo

The complex networks approach has been gaining popularity in analysing investor behaviour and stock markets, but within this approach, initial public offerings (IPO) have barely been explored.

Open the Black Box Data-Driven Explanation of Black Box Decision Systems

no code implementations26 Jun 2018 Dino Pedreschi, Fosca Giannotti, Riccardo Guidotti, Anna Monreale, Luca Pappalardo, Salvatore Ruggieri, Franco Turini

We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions: (i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation; (ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.

Decision Making

Local Rule-Based Explanations of Black Box Decision Systems

1 code implementation28 May 2018 Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, Fosca Giannotti

Then it derives from the logic of the local interpretable predictor a meaningful explanation consisting of: a decision rule, which explains the reasons of the decision; and a set of counterfactual rules, suggesting the changes in the instance's features that lead to a different outcome.

counterfactual

PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach

1 code implementation14 Feb 2018 Luca Pappalardo, Paolo Cintia, Paolo Ferragina, Emanuele Massucco, Dino Pedreschi, Fosca Giannotti

The problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e. g., tackles, passes, shots, etc.).

BIG-bench Machine Learning

A Survey Of Methods For Explaining Black Box Models

no code implementations6 Feb 2018 Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Dino Pedreschi, Fosca Giannotti

The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, delineating explicitly or implicitly its own definition of interpretability and explanation.

General Classification

NDlib: a Python Library to Model and Analyze Diffusion Processes Over Complex Networks

1 code implementation15 Dec 2017 Giulio Rossetti, Letizia Milli, Salvatore Rinzivillo, Alina Sirbu, Fosca Giannotti, Dino Pedreschi

Nowadays the analysis of dynamics of and on networks represents a hot topic in the Social Network Analysis playground.

Social and Information Networks 05C85, 60J60, 90C35 G.2.2; F.2.1

Human Perception of Performance

no code implementations5 Dec 2017 Luca Pappalardo, Paolo Cintia, Dino Pedreschi, Fosca Giannotti, Albert-Laszlo Barabasi

Humans are routinely asked to evaluate the performance of other individuals, separating success from failure and affecting outcomes from science to education and sports.

The Inductive Constraint Programming Loop

no code implementations12 Oct 2015 Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen, Barry O'Sullivan, Anastasia Paparrizou, Dino Pedreschi, Helmut Simonis

Constraint programming is used for a variety of real-world optimisation problems, such as planning, scheduling and resource allocation problems.

BIG-bench Machine Learning Scheduling

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