21 citations · 109 across the 23 of their papers we have counts for
7 papers · 1 filter
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Daniel Cunnington, Alessandra Russo, Mark Law +2
Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems re…
On the Transferability of VAE Embeddings using Relational Knowledge with Semi-Supervision
Harald Strömfelt, Luke Dickens, Artur d'Avila Garcez +1
We propose a new model for relational VAE semi-supervision capable of balancing disentanglement and low complexity modelling of relations with different symbolic properties. We com…
FLAP -- A Federated Learning Framework for Attribute-based Access Control Policies
Amani Abu Jabal, Elisa Bertino, Jorge Lobo +3
Technology advances in areas such as sensors, IoT, and robotics, enable new collaborative applications (e.g., autonomous devices). A primary requirement for such collaborations is…
Induction and Exploitation of Subgoal Automata for Reinforcement Learning
Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2
In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induct…
Learning Diverse Representations for Fast Adaptation to Distribution Shift
Daniel Pace, Alessandra Russo, Murray Shanahan
The i.i.d. assumption is a useful idealization that underpins many successful approaches to supervised machine learning. However, its violation can lead to models that learn to exp…
The ILASP system for Inductive Learning of Answer Set Programs
Mark Law, Alessandra Russo, Krysia Broda
The goal of Inductive Logic Programming (ILP) is to learn a program that explains a set of examples in the context of some pre-existing background knowledge. Until recently, most r…