AI*IA 2018 – Advances in Artificial Intelligence
XVIIth International Conference of the Italian Association for Artificial Intelligence, Trento, Italy, November 20–23, 2018, Proceedings
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Article
Open AccessGlitter or gold? Deriving structured insights from sustainability reports via large language models
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 Environment...
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Article
Open AccessThe SQUID Controller Unit for the LiteBIRD Space Mission: Description, Functional Tests and Early Performance Assessment
LiteBIRD is a satellite mission to be launched by JAXA in the early 2030s. It will measure the Cosmic Microwave Background (CMB) primordial B-modes with an unprecedented sensitivity. Microwave radiation will b...
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Article
Machine learning for microbiologists
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 applicat...
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Open AccessAdaptation of student behavioural routines during Covid-19: a multimodal approach
One population group that had to significantly adapt and change their behaviour during the COVID-19 pandemic is students. While previous studies have extensively investigated the impact of the pandemic on thei...
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Open AccessSynthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
Being able to provide counterfactual interventions—sequences of actions we would have had to take for a desirable outcome to happen—is essential to explain how to change an unfavourable decision by a black-box...
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Chapter and Conference Paper
Generalising via Meta-examples for Continual Learning in the Wild
Future deep learning systems call for techniques that can deal with the evolving nature of temporal data and scarcity of annotations when new problems occur. As a step towards this goal, we present FUSION (Few...
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Article
Open AccessHuman-in-the-loop handling of knowledge drift
We introduce and study knowledge drift (KD), a special form of concept drift that occurs in hierarchical classification. Under KD the vocabulary of concepts, their individual distributions, and the is-a relations...
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Chapter and Conference Paper
Catastrophic Forgetting in Continual Concept Bottleneck Models
Almost all Deep Learning models are dramatically affected by Catastrophic Forgetting when learning over continual streams of data. To mitigate this problem, several strategies for Continual Learning have been ...
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Open AccessAn efficient procedure for mining egocentric temporal motifs
Temporal graphs are structures which model relational data between entities that change over time. Due to the complex structure of data, mining statistically significant temporal subgraphs, also known as tempo...
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Open AccessGive more data, awareness and control to individual citizens, and they will help COVID-19 containment
The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restric...
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Open AccessA review and experimental analysis of active learning over crowdsourced data
Training data creation is increasingly a key bottleneck for develo** machine learning, especially for deep learning systems. Active learning provides a cost-effective means for creating training data by sele...
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Open AccessTowards Visual Semantics
Lexical Semantics is concerned with how words encode mental representations of the world, i.e., concepts. We call this type of concepts, classification concepts. In this paper, we focus on Visual Semantics, namel...
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Open AccessPutting human behavior predictability in context
Various studies have investigated the predictability of different aspects of human behavior such as mobility patterns, social interactions, and shop** and online behaviors. However, the existing researches h...
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Dealing with Mislabeling via Interactive Machine Learning
We propose an interactive machine learning framework where the machine questions the user feedback when it realizes it is inconsistent with the knowledge previously accumulated. The key idea is that the machin...
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Counts-of-counts similarity for prediction and search in relational data
Defining appropriate distance functions is a crucial aspect of effective and efficient similarity-based prediction and retrieval. Relational data are especially challenging in this regard. By viewing relationa...
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Open AccessCombining learning and constraints for genome-wide protein annotation
The advent of high-throughput experimental techniques paved the way to genome-wide computational analysis and predictive annotation studies. When considering the joint annotation of a large set of related enti...
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Chapter and Conference Paper
Automating Layout Synthesis with Constructive Preference Elicitation
Layout synthesis refers to the problem of arranging objects subject to design preferences and structural constraints. Applications include furniture arrangement, space partitioning (e.g. subdividing a house in...
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Book and Conference Proceedings
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Chapter and Conference Paper
Constructive Preference Elicitation for Multiple Users with Setwise Max-margin
In this paper we consider the problem of simultaneously eliciting the preferences of a group of users in an interactive way. We focus on constructive recommendation tasks, where the instance to be recommended sho...
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Guest editors’ introduction to the EcmlPkdd 2016 journal track special issue of Machine Learning
