Iryna Gurevych, professor of Computer Science and director of the Ubiquitous Knowledge Processing Lab at the Technical University of Darmstadt in Germany will give a keynote at the closing session of the 4th TAILOR conference “Trustworthy AI from Lab to market”, 4-5 June 2024, Lisbon. Her talk will be about misinformation and fact checking with LLM:s.
Keynote by Iryna Gurevych at the 4th TAILOR conference Read More »
The 33rd edition of the International Joint Conference of Artificial Intelligence (IJCAI) is organized at Jeju Island, in South Korea, from 03.08.24 to 09.08.24. We are thrilled to announce that some TAILOR scientists will be participating in this prestigious conference, presenting papers and more. Here below is the list of accepted papers, workshops and tutorials
TAILOR Scientists at IJCAI 2024 – Jeju Island Read More »
On April 8th and 9th, a workshop was held to further develop the TAILOR SRIR version 2. The SRIR is the Strategic Research & Innovation Roadmap, and the goal was to build upon the first version and many additional inputs. The main challenge was to define a suitable structure so that the large amount of inputs
Pictures from the workshop “TAILOR SRIR version 2” Read More »
Tom Viering Assistant Professor at Tu Delft Abstract: Machine learning usually requires large amounts of data to work well. But how much data is exactly required for sufficient performance? This can be estimated using learning curves. Learning curves plot a machine learning model’s performance versus the amount of data it is trained on. Note: the
Learning Curve Extrapolation with PFN’s Read More »
Jiarong Pan PhD at Bosch Center for Artificial Intelligence Abstract: Many real-world applications consider multiple objectives, potentially competing ones. For instance, for a model deciding whether to grant or deny loans, ensuring accurate while fair decisions is critical. Multi-objective Bayesian optimization (MOBO) is a sample-efficient technique for optimizing an expensive black-box function across multiple objectives.
Meta-learning for scalable multi-objective Bayesian optimization Read More »
Annelot Bosman PhD at Universiteit Leiden This project aims to investigate fairness from a new perspect- ive, namely by using robustness distributions, introduced in previous work. Investig- ating robustness in neural networks is very computationally expensive and as such the community has directed focus on increasing verification speed. Robustness distributions, although expensive to obtain, have
Using robustness distributions to better understand fairness in Neural Net-works Read More »
Every month, we want to acknowledge some valuable TAILOR papers, selected among the papers published by scientists belonging to our network by TAILOR principal investigator Fredrik Heintz.The list of the most valuable papers gathers contributions from different TAILOR partners, each providing valuable insights on different topics related to TrustworthyAI.Stay tuned for other valuable insights and
TAILOR Selected Papers: April Read More »
Marko Tesic Post-doc at LCFI, University of Cambridge, UK Recent advancements in AI, particularly in language modeling, have rekindled concerns about the potential automation of certain roles within the human workforce. To better understand which roles are susceptible to automation and to ensure the trustworthy and reliable deployment of AI, I aim to establish a
Lorenzo Pacchiardi Post-doc at University of Cambridge Artificial Intelligence (AI) systems (such as reinforcement-learning agents and Large Language Models, or LLMs) are typically evaluated by testing them on a benchmark and reporting an aggregated score. As benchmarks are constituted of instances demanding various capability levels to be completed, the aggregated score is uninformative of the
Evaluation of cognitive capabilities for LLMs Read More »
Davide Ceolin, Piek Vossen, Ilia Markov, Catholijn Jonker, Pradeep Murukannaiah Senior Researcher (Ceolin), Full Professor (Vossen, Jonker), Assistant Professor (Markov, Murukannaiah) Over the past years, foundational models, including large-language models and multi-modal systems, have significantly advanced the possibilities regarding the understanding, analysis, and generation of human language. However, from the extensive and widespread use of
Large Language Models for Media and Democracy: Wrecking or Saving Society? Read More »
