b'Stefano Teso'

research

∙ 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...

0 Emanuele Marconato, et al. ∙

research

∙ 03/22/2023

Neuro-Symbolic Reasoning Shortcuts: Mitigation Strategies and their Limitations

Neuro-symbolic predictors learn a mapping from sub-symbolic inputs to hi...

0 Emanuele Marconato, et al. ∙

research

∙ 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...

0 Emanuele Marconato, et al. ∙

research

∙ 07/29/2022

Leveraging Explanations in Interactive Machine Learning: An Overview

Explanations have gained an increasing level of interest in the AI and M...

0 Stefano Teso, et al. ∙

research

∙ 06/01/2022

Semantic Probabilistic Layers for Neuro-Symbolic Learning

We design a predictive layer for structured-output prediction (SOP) that...

0 Kareem Ahmed, et al. ∙

research

∙ 05/31/2022

Concept-level Debugging of Part-Prototype Networks

Part-prototype Networks (ProtoPNets) are concept-based classifiers desig...

0 Andrea Bontempelli, et al. ∙

research

∙ 05/31/2022

GlanceNets: Interpretabile, Leak-proof Concept-based Models

There is growing interest in concept-based models (CBMs) that combine hi...

0 Emanuele Marconato, et al. ∙

research

∙ 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...

0 Stefano Teso, et al. ∙

research

∙ 02/17/2022

Efficient and Reliable Probabilistic Interactive Learning with Structured Outputs

In this position paper, we study interactive learning for structured out...

0 Stefano Teso, et al. ∙

research

∙ 02/08/2022

Learning MAX-SAT from Contextual Examples for Combinatorial Optimisation

Combinatorial optimisation problems are ubiquitous in artificial intelli...

0 Mohit Kumar, et al. ∙

research

∙ 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...

11 Ivan Donadello, et al. ∙

research

∙ 09/23/2021

Toward a Unified Framework for Debugging Gray-box Models

We are concerned with debugging concept-based gray-box models (GBMs). Th...

0 Andrea Bontempelli, et al. ∙

research

∙ 07/15/2021

Learning Mixed-Integer Linear Programs from Contextual Examples

Mixed-integer linear programs (MILPs) are widely used in artificial inte...

0 Mohit Kumar, et al. ∙

research

∙ 06/07/2021

Interactive Label Cleaning with Example-based Explanations

We tackle sequential learning under label noise in applications where a ...

0 Stefano Teso, et al. ∙

research

∙ 03/31/2021

Neuro-Symbolic Constraint Programming for Structured Prediction

We propose Nester, a method for injecting neural networks into constrain...

0 Paolo Dragone, et al. ∙

research

∙ 03/27/2021

Human-in-the-loop Handling of Knowledge Drift

We introduce and study knowledge drift (KD), a complex form of drift tha...

0 Andrea Bontempelli, et al. ∙

research

∙ 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...

0 Antonio Vergari, et al. ∙

research

∙ 02/07/2021

Bandits for Learning to Explain from Explanations

We introduce Explearn, an online algorithm that learns to jointly output...

0 Freya Behrens, et al. ∙

research

∙ 11/19/2020

Multi-Modal Subjective Context Modelling and Recognition

Applications like personal assistants need to be aware ofthe user's cont...

0 Qiang Shen, et al. ∙

research

∙ 11/02/2020

Learning in the Wild with Incremental Skeptical Gaussian Processes

The ability to learn from human supervision is fundamental for personal ...

0 Andrea Bontempelli, et al. ∙

research

∙ 09/21/2020

Machine Guides, Human Supervises: Interactive Learning with Global Explanations

We introduce explanatory guided learning (XGL), a novel interactive lear...

11 Teodora Popordanoska, et al. ∙

research

∙ 07/26/2020

Efficient Generation of Structured Objects with Constrained Adversarial Networks

Generative Adversarial Networks (GANs) struggle to generate structured o...

0 Luca Di Liello, et al. ∙

research

∙ 07/20/2020

Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning

Recent work has demonstrated the promise of combining local explanations...

32 Teodora Popordanoska, et al. ∙

research

∙ 04/23/2020

Human-Machine Collaboration for Democratizing Data Science

Everybody wants to analyse their data, but only few posses the data scie...

22 Clément Gautrais, et al. ∙

research

∙ 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...

24 Patrick Schramowski, et al. ∙

research

∙ 12/19/2019

Does Symbolic Knowledge Prevent Adversarial Fooling?

Arguments in favor of injecting symbolic knowledge into neural architect...

0 Stefano Teso, et al. ∙

research

∙ 05/29/2018

Automating Personnel Rostering by Learning Constraints Using Tensors

Many problems in operations research require that constraints be specifi...

0 Mohit Kumar, et al. ∙

research

∙ 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...

0 Stefano Teso, et al. ∙

research

∙ 11/22/2017

Decomposition Strategies for Constructive Preference Elicitation

We tackle the problem of constructive preference elicitation, that is th...

0 Paolo Dragone, et al. ∙

research

∙ 11/21/2017

Constructive Preference Elicitation over Hybrid Combinatorial Spaces

Peference elicitation is the task of suggesting a highly preferred confi...

0 Paolo Dragone, et al. ∙

research

∙ 12/06/2016

Coactive Critiquing: Elicitation of Preferences and Features

When faced with complex choices, users refine their own preference crite...

0 Stefano Teso, et al. ∙

research

∙ 04/20/2016

Constructive Preference Elicitation by Setwise Max-margin Learning

In this paper we propose an approach to preference elicitation that is s...

0 Stefano Teso, et al. ∙

research

∙ 05/07/2014

Structured Learning Modulo Theories

Modelling problems containing a mixture of Boolean and numerical variabl...

0 Stefano Teso, et al. ∙

research

∙ 02/18/2014

Hybrid SRL with Optimization Modulo Theories

Generally speaking, the goal of constructive learning could be seen as, ...

0 Stefano Teso, et al. ∙

Stefano Teso

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