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  1. Article ; Online: Personalized bundle recommendation using preference elicitation and the Choquet integral.

    Robbi, Erich / Bronzini, Marco / Viappiani, Paolo / Passerini, Andrea

    Frontiers in artificial intelligence

    2024  Volume 7, Page(s) 1346684

    Abstract: Bundle recommendation aims to generate bundles of associated products that users tend to consume as a whole under certain circumstances. Modeling the bundle utility for users is a non-trivial task, as it requires to account for the potential ... ...

    Abstract Bundle recommendation aims to generate bundles of associated products that users tend to consume as a whole under certain circumstances. Modeling the bundle utility for users is a non-trivial task, as it requires to account for the potential interdependencies between bundle attributes. To address this challenge, we introduce a new preference-based approach for bundle recommendation exploiting the Choquet integral. This allows us to formalize preferences for coalitions of environmental-related attributes, thus recommending product bundles accounting for synergies among product attributes. An experimental evaluation of a dataset of local food products in Northern Italy shows how the Choquet integral allows the natural formalization of a sensible notion of environmental friendliness and that standard approaches based on weighted sums of attributes end up recommending bundles with lower environmental friendliness even if weights are explicitly learned to maximize it. We further show how preference elicitation strategies can be leveraged to acquire weights of the Choquet integral from user feedback in terms of preferences over candidate bundles, and show how a handful of queries allow to recommend optimal bundles for a diverse set of user prototypes.
    Language English
    Publishing date 2024-02-14
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-8212
    ISSN (online) 2624-8212
    DOI 10.3389/frai.2024.1346684
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Interpretability Is in the Mind of the Beholder: A Causal Framework for Human-Interpretable Representation Learning.

    Marconato, Emanuele / Passerini, Andrea / Teso, Stefano

    Entropy (Basel, Switzerland)

    2023  Volume 25, Issue 12

    Abstract: Research on Explainable Artificial Intelligence has recently started exploring the idea of producing explanations that, rather than being expressed in terms of low-level features, are encoded in terms ... ...

    Abstract Research on Explainable Artificial Intelligence has recently started exploring the idea of producing explanations that, rather than being expressed in terms of low-level features, are encoded in terms of
    Language English
    Publishing date 2023-11-22
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2014734-X
    ISSN 1099-4300 ; 1099-4300
    ISSN (online) 1099-4300
    ISSN 1099-4300
    DOI 10.3390/e25121574
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Sensory and multisensory reasoning: Is Bayesian updating modality-dependent?

    Fait, Stefano / Pighin, Stefania / Passerini, Andrea / Pavani, Francesco / Tentori, Katya

    Cognition

    2023  Volume 234, Page(s) 105355

    Abstract: Bayesianism assumes that probabilistic updating does not depend on the sensory modality by which information is processed. In this study, we investigate whether probability judgments based on visual and auditory information conform to this assumption. In ...

    Abstract Bayesianism assumes that probabilistic updating does not depend on the sensory modality by which information is processed. In this study, we investigate whether probability judgments based on visual and auditory information conform to this assumption. In a series of five experiments, we found that this is indeed the case when information is acquired through a single modality (i.e., only auditory or only visual) but not necessarily so when it comes from multiple modalities (i.e., audio-visual). In the latter case, judgments prove more accurate when both visual and auditory information individually support (i.e., increase the probability of) the hypothesis they also jointly support (synergy condition) than when either visual or auditory information support one hypothesis that is not the one they jointly support (contrast condition). In the extreme case in which both visual and auditory information individually support an alternative hypothesis to the one they jointly support (i.e., double-contrast condition), participants' accuracy is not only lower than in the synergy condition but near chance. This synergy-contrast effect represents a violation of the assumption that information modality is irrelevant for Bayesian updating and indicates an important limitation of multisensory integration, one which has not been previously documented.
    MeSH term(s) Humans ; Visual Perception ; Auditory Perception ; Bayes Theorem ; Problem Solving ; Judgment ; Acoustic Stimulation ; Photic Stimulation
    Language English
    Publishing date 2023-02-13
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1499940-7
    ISSN 1873-7838 ; 0010-0277
    ISSN (online) 1873-7838
    ISSN 0010-0277
    DOI 10.1016/j.cognition.2022.105355
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  4. Book ; Online: Egocentric Hierarchical Visual Semantics

    Erculiani, Luca / Bontempelli, Andrea / Passerini, Andrea / Giunchiglia, Fausto

    2023  

    Abstract: We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow a process which resembles that followed by humans when thinking of an ... ...

    Abstract We are interested in aligning how people think about objects and what machines perceive, meaning by this the fact that object recognition, as performed by a machine, should follow a process which resembles that followed by humans when thinking of an object associated with a certain concept. The ultimate goal is to build systems which can meaningfully interact with their users, describing what they perceive in the users' own terms. As from the field of Lexical Semantics, humans organize the meaning of words in hierarchies where the meaning of, e.g., a noun, is defined in terms of the meaning of a more general noun, its genus, and of one or more differentiating properties, its differentia. The main tenet of this paper is that object recognition should implement a hierarchical process which follows the hierarchical semantic structure used to define the meaning of words. We achieve this goal by implementing an algorithm which, for any object, recursively recognizes its visual genus and its visual differentia. In other words, the recognition of an object is decomposed in a sequence of steps where the locally relevant visual features are recognized. This paper presents the algorithm and a first evaluation.

    Comment: 10 pages, 5 figures, Accepted for publication at The second International Conference on Hybrid Human-Artificial Intelligence (HHAI2023)
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 401
    Publishing date 2023-05-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Interval Logic Tensor Networks

    Badreddine, Samy / Apriceno, Gianluca / Passerini, Andrea / Serafini, Luciano

    2023  

    Abstract: In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data. We interpret connectives using fuzzy logic, event ... ...

    Abstract In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data. We interpret connectives using fuzzy logic, event durations using trapezoidal fuzzy intervals, and fuzzy temporal relations using relationships between the intervals' areas. We propose Interval Logic Tensor Networks (ILTN), a neuro-symbolic system that learns by propagating gradients through IRL. In order to support effective learning, ILTN defines smoothened versions of the fuzzy intervals and temporal relations of IRL using softplus activations. We show that ILTN can successfully leverage knowledge expressed in IRL in synthetic tasks that require reasoning about events to predict their fuzzy durations. Our results show that the system is capable of making events compliant with background temporal knowledge.
    Keywords Computer Science - Artificial Intelligence
    Publishing date 2023-03-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Meta-Path Learning for Multi-relational Graph Neural Networks

    Ferrini, Francesco / Longa, Antonio / Passerini, Andrea / Jaeger, Manfred

    2023  

    Abstract: Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta- ... ...

    Abstract Existing multi-relational graph neural networks use one of two strategies for identifying informative relations: either they reduce this problem to low-level weight learning, or they rely on handcrafted chains of relational dependencies, called meta-paths. However, the former approach faces challenges in the presence of many relations (e.g., knowledge graphs), while the latter requires substantial domain expertise to identify relevant meta-paths. In this work we propose a novel approach to learn meta-paths and meta-path GNNs that are highly accurate based on a small number of informative meta-paths. Key element of our approach is a scoring function for measuring the potential informativeness of a relation in the incremental construction of the meta-path. Our experimental evaluation shows that the approach manages to correctly identify relevant meta-paths even with a large number of relations, and substantially outperforms existing multi-relational GNNs on synthetic and real-world experiments.
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-09-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Not All Neuro-Symbolic Concepts Are Created Equal

    Marconato, Emanuele / Teso, Stefano / Vergari, Antonio / Passerini, Andrea

    Analysis and Mitigation of Reasoning Shortcuts

    2023  

    Abstract: Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labels that are consistent with some prior knowledge by reasoning over high- ... ...

    Abstract Neuro-Symbolic (NeSy) predictive models hold the promise of improved compliance with given constraints, systematic generalization, and interpretability, as they allow to infer labels that are consistent with some prior knowledge by reasoning over high-level concepts extracted from sub-symbolic inputs. It was recently shown that NeSy predictors are affected by reasoning shortcuts: they can attain high accuracy but by leveraging concepts with unintended semantics, thus coming short of their promised advantages. Yet, a systematic characterization of reasoning shortcuts and of potential mitigation strategies is missing. This work fills this gap by characterizing them as unintended optima of the learning objective and identifying four key conditions behind their occurrence. Based on this, we derive several natural mitigation strategies, and analyze their efficacy both theoretically and empirically. Our analysis shows reasoning shortcuts are difficult to deal with, casting doubts on the trustworthiness and interpretability of existing NeSy solutions.

    Comment: 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2023-05-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Machine learning for microbiologists.

    Asnicar, Francesco / Thomas, Andrew Maltez / Passerini, Andrea / Waldron, Levi / Segata, Nicola

    Nature reviews. Microbiology

    2023  Volume 22, Issue 4, Page(s) 191–205

    Abstract: Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding ... ...

    Abstract Machine learning is increasingly important in microbiology where it is used for tasks such as predicting antibiotic resistance and associating human microbiome features with complex host diseases. The applications in microbiology are quickly expanding and the machine learning tools frequently used in basic and clinical research range from classification and regression to clustering and dimensionality reduction. In this Review, we examine the main machine learning concepts, tasks and applications that are relevant for experimental and clinical microbiologists. We provide the minimal toolbox for a microbiologist to be able to understand, interpret and use machine learning in their experimental and translational activities.
    MeSH term(s) Humans ; Machine Learning ; Microbiota
    Language English
    Publishing date 2023-11-15
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 2139054-X
    ISSN 1740-1534 ; 1740-1526
    ISSN (online) 1740-1534
    ISSN 1740-1526
    DOI 10.1038/s41579-023-00984-1
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5865: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)
    Zs.MG 74: Show issues

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  9. Book ; Online: Glitter or Gold? Deriving Structured Insights from Sustainability Reports via Large Language Models

    Bronzini, Marco / Nicolini, Carlo / Lepri, Bruno / Passerini, Andrea / Staiano, Jacopo

    2023  

    Abstract: Over the last decade, several regulatory bodies have started requiring the disclosure of non-financial information from publicly listed companies, in light of the investors' increasing attention to Environmental, Social, and Governance (ESG) issues. ... ...

    Abstract Over the last decade, several regulatory bodies have started requiring the disclosure of non-financial information from publicly listed companies, in light of the investors' increasing attention to Environmental, Social, and Governance (ESG) issues. Publicly released information on sustainability practices is often disclosed in diverse, unstructured, and multi-modal documentation. This poses a challenge in efficiently gathering and aligning the data into a unified framework to derive insights related to Corporate Social Responsibility (CSR). Thus, using Information Extraction (IE) methods becomes an intuitive choice for delivering insightful and actionable data to stakeholders. In this study, we employ Large Language Models (LLMs), In-Context Learning, and the Retrieval-Augmented Generation (RAG) paradigm to extract structured insights related to ESG aspects from companies' sustainability reports. We then leverage graph-based representations to conduct statistical analyses concerning the extracted insights. These analyses revealed that ESG criteria cover a wide range of topics, exceeding 500, often beyond those considered in existing categorizations, and are addressed by companies through a variety of initiatives. Moreover, disclosure similarities emerged among companies from the same region or sector, validating ongoing hypotheses in the ESG literature. Lastly, by incorporating additional company attributes into our analyses, we investigated which factors impact the most on companies' ESG ratings, showing that ESG disclosure affects the obtained ratings more than other financial or company data.
    Keywords Computer Science - Computation and Language ; Computer Science - Computational Engineering ; Finance ; and Science ; Computer Science - Computers and Society
    Publishing date 2023-10-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: Enhancing SMT-based Weighted Model Integration by Structure Awareness

    Spallitta, Giuseppe / Masina, Gabriele / Morettin, Paolo / Passerini, Andrea / Sebastiani, Roberto

    2023  

    Abstract: The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research. Whereas substantial progress has been made in dealing with purely discrete or purely continuous domains, ...

    Abstract The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research. Whereas substantial progress has been made in dealing with purely discrete or purely continuous domains, adapting the developed solutions to tackle hybrid domains, characterised by discrete and continuous variables and their relationships, is highly non-trivial. Weighted Model Integration (WMI) recently emerged as a unifying formalism for probabilistic inference in hybrid domains. Despite a considerable amount of recent work, allowing WMI algorithms to scale with the complexity of the hybrid problem is still a challenge. In this paper we highlight some substantial limitations of existing state-of-the-art solutions, and develop an algorithm that combines SMT-based enumeration, an efficient technique in formal verification, with an effective encoding of the problem structure. This allows our algorithm to avoid generating redundant models, resulting in drastic computational savings. Additionally, we show how SMT-based approaches can seamlessly deal with different integration techniques, both exact and approximate, significantly expanding the set of problems that can be tackled by WMI technology. An extensive experimental evaluation on both synthetic and real-world datasets confirms the substantial advantage of the proposed solution over existing alternatives. The application potential of this technology is further showcased on a prototypical task aimed at verifying the fairness of probabilistic programs.
    Keywords Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-02-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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