6 citations · 7 across the 4 of their papers we have counts for
6 papers
Meta-Learning Transformers to Improve In-Context Generalization
Lorenzo Braccaioli, Anna Vettoruzzo, Prabhant Singh +3
In-context learning enables transformer models to generalize to new tasks based solely on input prompts, without any need for weight updates. However, existing training paradigms t…
Learning to Learn without Forgetting using Attention
Anna Vettoruzzo, Joaquin Vanschoren, Mohamed-Rafik Bouguelia +1
Continual learning (CL) refers to the ability to continually learn over time by accommodating new knowledge while retaining previously learned experience. While this concept is inh…
Personalized Federated Learning with Contextual Modulation and Meta-Learning
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Thorsteinn Rögnvaldsson
Federated learning has emerged as a promising approach for training machine learning models on decentralized data sources while preserving data privacy. However, challenges such as…
Advances and Challenges in Meta-Learning: A Technical Review
Anna Vettoruzzo, Mohamed-Rafik Bouguelia, Joaquin Vanschoren +2
Meta-learning empowers learning systems with the ability to acquire knowledge from multiple tasks, enabling faster adaptation and generalization to new tasks. This review provides…
Wisdom of the Contexts: Active Ensemble Learning for Contextual Anomaly Detection
Ece Calikus, Slawomir Nowaczyk, Mohamed-Rafik Bouguelia +1
In contextual anomaly detection, an object is only considered anomalous within a specific context. Most existing methods for CAD use a single context based on a set of user-specifi…
Testing exchangeability with martingale for change-point detection
Liang Dai, Mohamed-Rafik Bouguelia
This work proposes a new exchangeability test for a random sequence through a martingale based approach. Its main contributions include: 1) an additive martingale which is more ame…