65 citations · 150 across the 13 of their papers we have counts for
13 papers
Grammar-Guided Evolutionary Search for Discrete Prompt Optimisation
Muzhaffar Hazman, Minh-Khoi Pham, Shweta Soundararajan +10
Prompt engineering has proven to be a crucial step in leveraging pretrained large language models (LLMs) in solving various real-world tasks. Numerous solutions have been proposed…
SMOSE: Sparse Mixture of Shallow Experts for Interpretable Reinforcement Learning in Continuous Control Tasks
Mátyás Vincze, Laura Ferrarotti, Leonardo Lucio Custode +2
Continuous control tasks often involve high-dimensional, dynamic, and non-linear environments. State-of-the-art performance in these tasks is achieved through complex closed-box po…
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-Training
Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
Network pruning focuses on algorithms that aim to reduce a given model's computational cost by removing a subset of its parameters while having minimal impact on performance. Throu…
An investigation on the use of Large Language Models for hyperparameter tuning in Evolutionary Algorithms
Leonardo Lucio Custode, Fabio Caraffini, Anil Yaman +1
Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization pr…
Many-Objective Evolutionary Influence Maximization: Balancing Spread, Budget, Fairness, and Time
Elia Cunegatti, Leonardo Lucio Custode, Giovanni Iacca
The Influence Maximization (IM) problem seeks to discover the set of nodes in a graph that can spread the information propagation at most. This problem is known to be NP-hard, and…
Social Interpretable Reinforcement Learning
Leonardo Lucio Custode, Giovanni Iacca
Reinforcement Learning (RL) bears the promise of being a game-changer in many applications. However, since most of the literature in the field is currently focused on opaque models…
Quality Diversity Evolutionary Learning of Decision Trees
Andrea Ferigo, Leonardo Lucio Custode, Giovanni Iacca
Addressing the need for explainable Machine Learning has emerged as one of the most important research directions in modern Artificial Intelligence (AI). While the current dominant…
Interpretable AI for policy-making in pandemics
Leonardo Lucio Custode, Giovanni Iacca
Since the first wave of the COVID-19 pandemic, governments have applied restrictions in order to slow down its spreading. However, creating such policies is hard, especially becaus…
Interpretable pipelines with evolutionarily optimized modules for RL tasks with visual inputs
Leonardo Lucio Custode, Giovanni Iacca
The importance of explainability in AI has become a pressing concern, for which several explainable AI (XAI) approaches have been recently proposed. However, most of the available…
Black-box adversarial attacks using Evolution Strategies
Hao Qiu, Leonardo Lucio Custode, Giovanni Iacca
In the last decade, deep neural networks have proven to be very powerful in computer vision tasks, starting a revolution in the computer vision and machine learning fields. However…
A Signal-Centric Perspective on the Evolution of Symbolic Communication
Quintino Francesco Lotito, Leonardo Lucio Custode, Giovanni Iacca
The evolution of symbolic communication is a longstanding open research question in biology. While some theories suggest that it originated from sub-symbolic communication (i.e., i…
Genetic Improvement of Routing Protocols for Delay Tolerant Networks
Michela Lorandi, Leonardo Lucio Custode, Giovanni Iacca
Routing plays a fundamental role in network applications, but it is especially challenging in Delay Tolerant Networks (DTNs). These are a kind of mobile ad hoc networks made of e.g…
Evolutionary learning of interpretable decision trees
Leonardo Lucio Custode, Giovanni Iacca
Reinforcement learning techniques achieved human-level performance in several tasks in the last decade. However, in recent years, the need for interpretability emerged: we want to…