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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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08/29/2023
How Faithful are Self-Explainable GNNs?
Self-explainable deep neural networks are a recent class of models that ...
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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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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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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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02/02/2023
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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07/29/2022
Leveraging Explanations in Interactive Machine Learning: An Overview
Explanations have gained an increasing level of interest in the AI and M...
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06/01/2022
Semantic Probabilistic Layers for Neuro-Symbolic Learning
We design a predictive layer for structured-output prediction (SOP) that...
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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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05/20/2022
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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02/17/2022
Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs
In this position paper, we study interactive learning for structured out...
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02/08/2022
Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation
Combinatorial optimisation problems are ubiquitous in artificial intelli...
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12/09/2021
Machine Learning for Utility Prediction in Argument-Based Computational Persuasion
Automated persuasion systems (APS) aim to persuade a user to believe som...
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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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07/15/2021
Learning Mixed-Integer Linear Programs from Contextual Examples
Mixed-integer linear programs (MILPs) are widely used in artificial inte...
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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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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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02/11/2021
A Compositional Atlas of Tractable Circuit Operations: From Simple Transformations to Complex Information-Theoretic Queries
Circuit representations are becoming the lingua franca to express and re...
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02/07/2021
Bandits for Learning to Explain from Explanations
We introduce Explearn, an online algorithm that learns to jointly output...
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11/19/2020
Multi-Modal Subjective Context Modelling and Recognition
Applications like personal assistants need to be aware ofthe user's cont...
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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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09/21/2020
Machine Guides, Human Supervises: Interactive Learning with Global Explanations
We introduce explanatory guided learning (XGL), a novel interactive lear...
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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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07/20/2020
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning
Recent work has demonstrated the promise of combining local explanations...
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04/23/2020
Human-Machine Collaboration for Democratizing Data Science
Everybody wants to analyse their data, but only few posses the data scie...
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01/15/2020
Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations
Deep neural networks have shown excellent performances in many real-worl...
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12/19/2019
Does Symbolic Knowledge Prevent Adversarial Fooling?
Arguments in favor of injecting symbolic knowledge into neural architect...
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05/29/2018
Automating Personnel Rostering by Learning Constraints Using Tensors
Many problems in operations research require that constraints be specifi...
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05/22/2018
"Why Should I Trust Interactive Learners?" Explaining Interactive Queries of Classifiers to Users
Although interactive learning puts the user into the loop, the learner r...
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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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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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05/07/2014
Structured Learning Modulo Theories
Modelling problems containing a mixture of Boolean and numerical variabl...
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02/18/2014