activity
20212026
most citedUsing Early-Learning Regularization to Classify Real-World Noisy Data

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2026

Low-Resource Preference Adaptation of LLMs via Activation-Based Label Propagation

Alessio Galatolo, Meriem Beloucif

Adapting large language models to user-specific preferences is often constrained by the cost of human annotation, making preference optimisation impractical in low-resource setting…

cs.RO2026

Lightweight Visual Reasoning for Socially-Aware Robots

Alessio Galatolo, Ronald Cumbal, Alexandros Rouchitsas +3

Robots operating in shared human environments must not only navigate, interact, and detect their surroundings, they must also interpret and respond to dynamic, and often unpredicta…

cs.AI2025

Beyond Ethical Alignment: Evaluating LLMs as Artificial Moral Assistants

Alessio Galatolo, Luca Alberto Rappuoli, Katie Winkle +1

The recent rise in popularity of large language models (LLMs) has prompted considerable concerns about their moral capabilities. Although considerable effort has been dedicated to…

cs.CL2025

Visualising Policy-Reward Interplay to Inform Zeroth-Order Preference Optimisation of Large Language Models

Alessio Galatolo, Zhenbang Dai, Katie Winkle +1

Fine-tuning Large Language Models (LLMs) with first-order methods like back-propagation is computationally intensive. Zeroth-Order (ZO) optimisation uses function evaluations inste…

cs.CV20211 cited

Using Early-Learning Regularization to Classify Real-World Noisy Data

Alessio Galatolo, Alfred Nilsson, Roderick Karlemstrand +1

The memorization problem is well-known in the field of computer vision. Liu et al. propose a technique called Early-Learning Regularization, which improves accuracy on the CIFAR da…