Learning Over Time
Spring ScHool
Let's Rethink Machine Learning Algorithms
Topics
Collectionless AI
Continual/Lifelong Learning
Reinforcement Learning
Time Series
Online Learning
Active Learning
Curriculum Learning
Domain Adaptation
Transfer Learning
In-Context Learning
A spring school on Machines that continuously learn over time (LOT)
...Beyond training, test and deploy
...Beyond datasets
...Beyond CENTRALIZED LEARNING
...Back to learning algorithms
what is lot about?
Time as the protagonist of learning
Sensory information is characterized by a natural temporal development of the data that is commonly neglected by current technologies. In nature, we do not learn from a huge dataset of "shuffled images", and we do not store our entire visual life, stochastically sampling from it. Interacting over time allows multiple agents to share information at different stages of their development, to grow their own skills in an appropriate manner, or to help other agents improve: we educate children in function of their skills at the current age...
Is there a problem with current AI technologies?
The growing ubiquity of Large Language Models (LLM) has recently opened strong debates on scenarios giving rise to potentially rogue AIs involving social and political aspects. The source of these debates is deeply connected with the exploitation of increasingly large data collections, which requires huge financial resources, thus leading to the centralization of information. This aspect produces undeniable privacy problems as well as very controversial geopolitical effects. In a nutshell: data centralization issues; privacy and geopolitical issues; energy efficiency issues; limited control, customizability, and causality.
Look forward!
The outstanding results of current Machine Learning-based models should be still massively leveraged by the current technologies. However, they are all based on previously collected datasets and networks trained by stochastically sampling from them, without any dynamic human intervention: humans are only bare data labelers, then they are out of the game.
Let's discuss these topics and other related ones, let's meet in Siena!
Let's define the new research directions that we need to follow to propose the AI technologies of the future!
See you in Siena!!! :)
Present your WORK AS A POSTER! [new]
You have the opportunity to showcase your work if it aligns with the topics of the school (see the list on this page).
Both already published papers and ongoing projects are welcome as posters. This is a great chance to interact with school participants, receive feedback, and discuss new ideas.
Submission Details:
Posters should be in A0 portrait format.
Conference posters are perfectly acceptable.
Please email title and abstract of your poster to lotschool@googlegroups.com
There are no proceedings, so don't worry! We will simply ensure the topic of your work is relevant to the school's themes.
when: March 24-27 2025
Timeline
Registration opens: 1st January 2025 (TBC)
Submission of poster proposals (deadline): 14th February 2025
Acceptance notifications: 21st February 2025
LOT school: 24-27 March 2025
VEnue: CErtosa di Pontignano, SIena, Italy
LECTURERS
Organizers
University of Siena
University of Pisa
University of Pisa
Scuola IMT Alti Studi Lucca
Italian Institute of Technology
contact us
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ARE YOU INTERESTED IN ATTENDING? LET US KNOW!
Further details in the Registration page