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Algorithms and Representations for Continual Learning

Cel

Massive data poses a fundamental challenge to machine learning (ML) since algorithm running times scale
with data size. As data grows faster than computational power, future problems can take longer to solve
without economizing training via continual learning (CL). However, CL can result in performance drops in
prior tasks, known as catastrophic forgetting (CF), a persistent issue in ML, including fine-tuning foundation
models (FMs), blocking their full potential.
Contrary to the dogma that increasing more resources are critical for future FMs, our recent research
shows that computational investments can be recovered near-optimally without CF, hinting at a sustainable,
future-proof ML paradigm. Our observation calls for new formulations in robust representation learning
that prevent catastrophic overfitting (CO) and new game-theoretic algorithms that avoid limit cycles (LC).
However, understanding the trade-offs between data and computation in CL is at its infancy, especially for
non-linear models. Existing methods are case-specific and do not rigorously offer sustainability, robustness, or
trustworthiness.
The proposed ERC project ARC addresses these challenges by establishing mathematical foundations to
manage continuous data growth without CF or CO or LC and to optimize dynamic representations with
new robust and reinforcement learning formulations. We build new adaptive algorithms as well as decision
verification techniques that optimally handle increasing data sizes as well as dynamic models while matching
theoretical runtime bounds. We demonstrate our rigorous theory and robust learning frameworks in real-world
scenarios, such as neuro-rehabilitation and circuit design, to validate methods and explore trade-offs across
domains.
Our approach views learning problems as dynamic and interactive, potentially revolutionizing ML by creating
holistic, sustainable systems with optimal performance across diverse applications under limited resources.

Dziedzina nauki (EuroSciVoc)

Projekt nie został jeszcze sklasyfikowany według klasyfikacji EuroSciVoc.
Wskaż dziedziny nauki, które twoim zdaniem są najbardziej istotne z punktu widzenia tego projektu i pomóż nam usprawnić naszą usługę klasyfikacji.

Słowa kluczowe

Program(-y)

Temat(-y)

System finansowania

HORIZON-ERC - HORIZON ERC Grants

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Zaproszenie do składania wniosków

(odnośnik otworzy się w nowym oknie) ERC-2025-ADG

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Instytucja przyjmująca

ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE
Wkład UE netto
€ 3 344 552,00
Adres
BATIMENT CE 3316 STATION 1
1015 Lausanne
Szwajcaria
Region
Schweiz/Suisse/Svizzera Région lémanique Vaud
Rodzaj działalności
Higher or Secondary Education Establishments
Linki
Koszt całkowity
Brak danych

Beneficjenci (1)