305 citations · 703 across the 28 of their papers we have counts for
10 papers · 1 filter
The Importance of Being Statistically Earnest: A Critical Re-evaluation of GSM-Symbolic
Dominika Agnieszka Długosz, Arlindo Oliveira, Natalia Díaz-Rodríguez
The GSM-Symbolic benchmark (Mirzadeh et al., 2025) reported consistent performance drops across 25 Large Language Models (LLMs) when tested on template-generated variants of GSM8K…
Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
Carmen Quiles-Ramírez, Leticia L. Rodríguez, Nicolás Martorell +1
In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples. Yet, how these models use the provided context remains o…
Towards a more efficient computation of individual attribute and policy contribution for post-hoc explanation of cooperative multi-agent systems using Myerson values
Giorgio Angelotti, Natalia Díaz-Rodríguez
A quantitative assessment of the global importance of an agent in a team is as valuable as gold for strategists, decision-makers, and sports coaches. Yet, retrieving this informati…
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values
Alexandre Heuillet, Fabien Couthouis, Natalia Díaz-Rodríguez
While Explainable Artificial Intelligence (XAI) is increasingly expanding more areas of application, little has been applied to make deep Reinforcement Learning (RL) more comprehen…
Explainability in Deep Reinforcement Learning
Alexandre Heuillet, Fabien Couthouis, Natalia Díaz-Rodríguez
A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature relevance techniques to explain a deep neural network (DNN) output or explaining mode…
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics
Stephane Doncieux, Nicolas Bredeche, Léni Le Goff +9
Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithm…
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Alejandro Barredo Arrieta, Natalia Díaz-Rodríguez, Javier Del Ser +9
In the last years, Artificial Intelligence (AI) has achieved a notable momentum that may deliver the best of expectations over many application sectors across the field. For this t…
Don't forget, there is more than forgetting: new metrics for Continual Learning
Natalia Díaz-Rodríguez, Vincenzo Lomonaco, David Filliat +1
Continual learning consists of algorithms that learn from a stream of data/tasks continuously and adaptively thought time, enabling the incremental development of ever more complex…
State Representation Learning for Control: An Overview
Timothée Lesort, Natalia Díaz-Rodríguez, Jean-François Goudou +1
Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representatio…
Unsupervised state representation learning with robotic priors: a robustness benchmark
Timothée Lesort, Mathieu Seurin, Xinrui Li +2
Our understanding of the world depends highly on our capacity to produce intuitive and simplified representations which can be easily used to solve problems. We reproduce this simp…