March 2022

Conformal Inference for multivariate, complex, and heterogeneous data

Marcos Matabuena University of Santiago de Compostela In this project, in collaboration with Gábor Lugosi (UPF), we propose new uncertainty quantification methods based on the design of new Conformal Inference strategies for complex data that arise in modern personalized medicine applications. The new uncertainty methods can examine the reliability and safety of results obtained with

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Private Continual Learning from a Stream of Pretrained Models

Antonio Carta Post-doc at Pisa University 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, effective and scalable way appears to be fundamental for a more sustainable development of Artificial Intelligent systems. However, access

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Matheuristic Techniques for Timetabling Problems

Roberto Maria Rosati PhD Student in Information Engineering at University of Udine Recently, matheuristics have emerged as a promising research branch in combinatorial optimization. Thanks to this collaboration supported by TAILOR connectivity fund, we will design and apply novel matheuristic techniques to a variety of timetabling problems that are under investigation at University of Udine.

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Trustworthy and sample efficient computer vision

Mohammadreza Amirian Research assistant, Zurich University of Applied Sciences (ZHAW) After the breakthrough of transformers in the context of natural language processing, these models are now being adapted for computer vision and image classification tasks. Transformer-based models showed at least equal descriptive properties compared with convolutional models, however, initial specimen required a larger amount of

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