Wendy Ju, associated professor at Cornell University, will give a keynote lecture at the opening session of the 4th TAILOR conference “Trustworthy AI from Lab to market”, 4-5 June 2024, Lisbon. Her talk will be about interaction intelligence.
Keynote by Wendy Ju Read More »
Muhammad Waseem Postdoctoral Researcher at Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland Large Language Models (LLMs) are advanced AI tools capable of understanding and generating human-like text, advancing various sectors, including healthcare. This project aims to enhance healthcare services using LLMs, focusing on improving their trustworthiness for clinical applications. Trustworthiness encompasses reliability, fairness,
Enhancing Trustworthiness in Healthcare Large Language Models Read More »
Giacomo Cignoni Research Fellow at the University of Pisa Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt and generalize continually, in an efficient way appears to be fundamental for a more sustainable development of Artificial Intelligent systems. However, research
Continual Self-Supervised Learning Read More »
Lennert De Smet PhD at KU Leuven Transparency and technical robustness are two fundamental requirements for AI systems following the European Union AI Act, especially in higher-risk domains. Transparency is intricately related to the notion of explainability, allowing an AI system to accurately describe the reasoning behind its predictions. Through such explanations does the system
Tractable and Explainable Probabilistic AI Read More »
Georgios Chalkiadakis Professor at Technical University of Crete Current mobility-as-a-service platforms have departed from the original objectives of the sharing economy-inspired social ridesharing paradigm: regrettably, they view drivers as taxi workers; focus on profit maximization rather than fair travel costs’ allocation; and disregard essential private preferences of users (relating for instance to their feeling of
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 »
