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09/14/2023
Interpretability is in the Mind of the Beholder: A Causal Framework for Human-interpretable Representation Learning
Focus in Explainable AI is shifting from explanations defined in terms o...
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08/11/2023
Learning to Guide Human Experts via Personalized Large Language Models
In learning to defer, a predictor identifies risky decisions and defers ...
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06/14/2023
Adaptation of Student Behavioural Routines during COVID-19: A Multimodal Approach
One population group that had to significantly adapt and change their be...
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05/31/2023
Not All Neuro-Symbolic Concepts Are Created Equal: Analysis and Mitigation of Reasoning Shortcuts
Neuro-Symbolic (NeSy) predictive models hold the promise of improved com...
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05/09/2023
Egocentric Hierarchical Visual Semantics
We are interested in aligning how people think about objects and what ma...
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03/31/2023
Interval Logic Tensor Networks
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logi...
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03/22/2023
Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations
Neuro-symbolic predictors learn a mapping from sub-symbolic inputs to hi...
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02/13/2023
Enhancing SMT-based Weighted Model Integration by Structure Awareness
The development of efficient exact and approximate algorithms for probab...
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02/02/2023
Neuro Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal
We introduce Neuro-Symbolic Continual Learning, where a model has to sol...
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02/02/2023
Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities
Graph Neural Networks (GNNs) have become the leading paradigm for learni...
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10/27/2022
Explaining the Explainers in Graph Neural Networks: a Comparative Study
Following a fast initial breakthrough in graph based learning, Graph Neu...
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10/13/2022
Global Explainability of GNNs via Logic Combination of Learned Concepts
While instance-level explanation of GNN is a well-studied problem with p...
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06/28/2022
SMT-based Weighted Model Integration with Structure Awareness
Weighted Model Integration (WMI) is a popular formalism aimed at unifyin...
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05/31/2022
Concept-level Debugging of Part-Prototype Networks
Part-prototype Networks (ProtoPNets) are concept-based classifiers desig...
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05/31/2022
GlanceNets: Interpretabile, Leak-proof Concept-based Models
There is growing interest in concept-based models (CBMs) that combine hi...
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05/27/2022
Generating personalized counterfactual interventions for algorithmic recourse by eliciting user preferences
Counterfactual interventions are a powerful tool to explain the decision...
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05/20/2022
Machine Learning for Combinatorial Optimisation of Partially-Specified Problems: Regret Minimisation as a Unifying Lens
It is increasingly common to solve combinatorial optimisation problems t...
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05/18/2022
Neighbourhood matching creates realistic surrogate temporal networks
Temporal networks are essential for modeling and understanding systems w...
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01/18/2022
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
Being able to provide counterfactual interventions - sequences of action...
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11/11/2021
The Science of Rejection: A Research Area for Human Computation
We motivate why the science of learning to reject model predictions is c...
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10/04/2021
An Efficient Procedure for Mining Egocentric Temporal Motifs
Temporal graphs are structures which model relational data between entit...
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09/23/2021
Toward a Unified Framework for Debugging Gray-box Models
We are concerned with debugging concept-based gray-box models (GBMs). Th...
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09/01/2021
Learning compositional programs with arguments and sampling
One of the most challenging goals in designing intelligent systems is em...
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06/07/2021
Interactive Label Cleaning with Example-based Explanations
We tackle sequential learning under label noise in applications where a ...
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04/26/2021
Towards Visual Semantics
In Visual Semantics we study how humans build mental representations, i....
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03/31/2021
Neuro-Symbolic Constraint Programming for Structured Prediction
We propose Nester, a method for injecting neural networks into constrain...
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03/27/2021
Human-in-the-loop Handling of Knowledge Drift
We introduce and study knowledge drift (KD), a complex form of drift tha...
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01/28/2021
Generalising via Meta-Examples for Continual Learning in the Wild
Learning quickly and continually is still an ambitious task for neural n...
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12/15/2020
Learning Aggregation Functions
Learning on sets is increasingly gaining attention in the machine learni...
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11/02/2020
Learning in the Wild with Incremental Skeptical Gaussian Processes
The ability to learn from human supervision is fundamental for personal ...
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09/17/2020
Few-Shot Unsupervised Continual Learning through Meta-Examples
In real-world applications, data do not reflect the ones commonly used f...
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07/26/2020
Efficient Generation of Structured Objects with Constrained Adversarial Networks
Generative Adversarial Networks (GANs) struggle to generate structured o...
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04/10/2020
Give 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...
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12/06/2019
Continual egocentric object recognition
We are interested in the problem of continual object recognition in a se...
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11/22/2017
Decomposition Strategies for Constructive Preference Elicitation
We tackle the problem of constructive preference elicitation, that is th...
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11/21/2017
Constructive Preference Elicitation over Hybrid Combinatorial Spaces
Peference elicitation is the task of suggesting a highly preferred confi...
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12/06/2016
Coactive Critiquing: Elicitation of Preferences and Features
When faced with complex choices, users refine their own preference crite...
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06/17/2016
Interpretability in Linear Brain Decoding
Improving the interpretability of brain decoding approaches is of primar...
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04/20/2016
Constructive Preference Elicitation by Setwise Max-margin Learning
In this paper we propose an approach to preference elicitation that is s...
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08/18/2015
Learning Modulo Theories for preference elicitation in hybrid domains
This paper introduces CLEO, a novel preference elicitation algorithm cap...
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05/07/2014
Structured Learning Modulo Theories
Modelling problems containing a mixture of Boolean and numerical variabl...
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02/18/2014
