Cellular Automata with Rare Events; Resolution of an Outstanding Problem in the Bootstrap Percolation Model. - Paolo De Gregorio; Aonghus Lawlor; Phil Bradley; Kenneth A. Dawson (ACRI, 2004)

Bootstrap Percolation. - Paolo De Gregorio; Aonghus Lawlor; Kenneth A. Dawson (Encyclopedia of Complexity and Systems Science, 2009)

City-scale traffic simulation from digital footprints. - Gavin McArdle; Aonghus Lawlor; Eoghan Furey; Alexei Pozdnoukhov (UrbComp@KDD, 2012)

An Analysis of Recommender Algorithms for Online News. - Doychin Doychev; Aonghus Lawlor; Rachael Rafter (CLEF (Working Notes), 2014)

Using Digital Footprints for a City-Scale Traffic Simulation. - Gavin McArdle; Eoghan Furey; Aonghus Lawlor; Alexei Pozdnoukhov (ACM Trans. Intell. Syst. Technol., 2014)

Great Explanations: Opinionated Explanations for Recommendations. - Khalil Muhammad; Aonghus Lawlor; Rachael Rafter; Barry Smyth (ICCBR, 2015)

Opinionated Explanations for Recommendation Systems. - Aonghus Lawlor; Khalil Muhammad; Rachael Rafter; Barry Smyth (SGAI Conf., 2015)

News Recommenders: Real-Time, Real-Life Experiences. - Doychin Doychev; Rachael Rafter; Aonghus Lawlor; Barry Smyth (UMAP, 2015)

Generating Personalised and Opinionated Review Summaries. - Khalil Muhammad; Aonghus Lawlor; Rachael Rafter; Barry Smyth (UMAP Workshops, 2015)

Explanation-based Ranking in Opinionated Recommender Systems. - Khalil Muhammad; Aonghus Lawlor; Barry Smyth (AICS, 2016)

On the Use of Opinionated Explanations to Rank and Justify Recommendations. - Khalil Muhammad; Aonghus Lawlor; Barry Smyth (FLAIRS, 2016)

Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies. - Sergio Oramas; Luis Espinosa Anke; Aonghus Lawlor; Xavier Serra; Horacio Saggion (ISMIR, 2016)

A Live-User Study of Opinionated Explanations for Recommender Systems. - Khalil Ibrahim Muhammad; Aonghus Lawlor; Barry Smyth (IUI, 2016)

On the Pros and Cons of Explanation-Based Ranking. - Khalil Muhammad; Aonghus Lawlor; Barry Smyth (ICCBR, 2017)

Running with Recommendation. - Jakim Berndsen; Aonghus Lawlor; Barry Smyth (HealthRecSys@RecSys, 2017)

A Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated Reviews. - Khalil Muhammad; Aonghus Lawlor; Barry Smyth (FLAIRS, 2018)

Automatic Generation of Natural Language Explanations. - Felipe Costa; Sixun Ouyang; Peter Dolog; Aonghus Lawlor (IUI Companion, 2018)

Improving Explainable Recommendations with Synthetic Reviews. - Sixun Ouyang; Aonghus Lawlor; Felipe Costa; Peter Dolog (CoRR, 2018)

Pace my race: recommendations for marathon running. - Jakim Berndsen; Barry Smyth; Aonghus Lawlor (RecSys, 2019)

PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy. - Erika Duriakova; Elias Z. Tragos; Barry Smyth; Neil Hurley; Francisco J. Peña; Panagiotis Symeonidis; James Geraci; Aonghus Lawlor (RecSys, 2019)

PyRecGym: a reinforcement learning gym for recommender systems. - Bichen Shi; Makbule Gulcin Ozsoy; Neil Hurley; Barry Smyth; Elias Z. Tragos; James Geraci; Aonghus Lawlor (RecSys, 2019)

UMAP 2019 Workshop on Explainable and Holistic User Modeling (ExHUM) Chairs' Welcome & Organization. - Cataldo Musto; Amon Rapp; Federica Cena; Frank Hopfgartner; Judy Kay; Aonghus Lawlor; Pasquale Lops; Giovanni Semeraro; Nava Tintarev (UMAP (Adjunct Publication), 2019)

NEAR: A Partner to Explain Any Factorised Recommender System. - Sixun Ouyang; Aonghus Lawlor (UMAP (Adjunct Publication), 2019)

Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans. - Ciara Feely; Brian Caulfield 001; Aonghus Lawlor; Barry Smyth (ICCBR, 2020)

A Collaborative Filtering Approach to Successfully Completing The Marathon. - Jakim Berndsen; Barry Smyth; Aonghus Lawlor (ICMLA, 2020)

FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems. - Khalil Muhammad; Qinqin Wang; Diarmuid O'Reilly-Morgan; Elias Z. Tragos; Barry Smyth; Neil Hurley; James Geraci; Aonghus Lawlor (KDD, 2020)

Mining Marathon Training Data to Generate Useful User Profiles. - Jakim Berndsen; Barry Smyth; Aonghus Lawlor (MLSA@PKDD/ECML, 2020)

An Algorithmic Framework for Decentralised Matrix Factorisation. - Erika Duriakova; Weipéng Huáng; Elias Z. Tragos; Aonghus Lawlor; Barry Smyth; James Geraci; Neil Hurley (ECML/PKDD (2), 2020)

Fit to Run: Personalised Recommendations for Marathon Training. - Jakim Berndsen; Barry Smyth; Aonghus Lawlor (RecSys, 2020)

Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners. - Ciara Feely; Brian Caulfield 001; Aonghus Lawlor; Barry Smyth (RecSys, 2020)

Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation. - Francisco J. Peña; Diarmuid O'Reilly-Morgan; Elias Z. Tragos; Neil Hurley; Erika Duriakova; Barry Smyth; Aonghus Lawlor (RecSys, 2020)

MP4Rec: Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks. - Makbule Gulcin Ozsoy; Diarmuid O'Reilly-Morgan; Panagiotis Symeonidis; Elias Z. Tragos; Neil Hurley; Barry Smyth; Aonghus Lawlor (IEEE Access, 2020)

Boosting the Training Time of Weakly Coordinated Distributed Machine Learning. - Erika Duriakova; Elias Z. Tragos; Aonghus Lawlor; Barry Smyth; Neil Hurley (IEEE BigData, 2021)

A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries. - Ciara Feely; Brian Caulfield 001; Aonghus Lawlor; Barry Smyth (ICCBR, 2021)

Optimising Knee Injury Detection with Spatial Attention and Validating Localisation Ability. - Niamh Belton; Ivan Welaratne; Adil Dahlan; Ronan T. Hearne; Misgina Tsighe Hagos; Aonghus Lawlor; Kathleen M. Curran (MIUA, 2021)

Improving Explainable Recommendations by Deep Review-Based Explanations. - Sixun Ouyang; Aonghus Lawlor (IEEE Access, 2021)

DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning. - Bichen Shi; Elias Z. Tragos; Makbule Gulcin Ozsoy; Ruihai Dong; Neil Hurley; Barry Smyth; Aonghus Lawlor (IEEE Access, 2021)

Semi-Supervised Siamese Network for Identifying Bad Data in Medical Imaging Datasets. - Niamh Belton; Aonghus Lawlor; Kathleen M. Curran (CoRR, 2021)

An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon Runners. - Ciara Feely; Brian Caulfield 001; Aonghus Lawlor; Barry Smyth (ICCBR, 2022)

MARF: User-Item Mutual Aware Representation with Feedback. - Qinqin Wang; Khalil Muhammad; Diarmuid O'Reilly-Morgan; Barry Smyth; Elias Z. Tragos; Aonghus Lawlor; Neil Hurley; Ruihai Dong (ICWE, 2022)

Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation. - Qinqin Wang; Elias Z. Tragos; Neil Hurley; Barry Smyth; Aonghus Lawlor; Ruihai Dong (UMAP, 2022)

Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems. - Qinqin Wang; Diarmuid O'Reilly-Morgan; Elias Z. Tragos; Neil Hurley; Barry Smyth; Aonghus Lawlor; Ruihai Dong (IEEE Access, 2022)

Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners. - Barry Smyth; Aonghus Lawlor; Jakim Berndsen; Ciara Feely (User Model. User Adapt. Interact., 2022)

FewSOME: One-Class Few Shot Anomaly Detection with Siamese Networks. - Niamh Belton; Misgina Tsighe Hagos; Aonghus Lawlor; Kathleen M. Curran (CVPR Workshops, 2023)

Item Graph Convolution Collaborative Filtering for Inductive Recommendations. - Edoardo D'Amico; Khalil Muhammad; Elias Z. Tragos; Barry Smyth; Neil Hurley; Aonghus Lawlor (ECIR (1), 2023)

Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems. - Aayush Singha Roy; Edoardo D'Amico; Aonghus Lawlor; Neil Hurley (FLAIRS, 2023)

Can We Transfer Noise Patterns? A Multi-environment Spectrum Analysis Model Using Generated Cases. - Haiwen Du; Zheng Ju; Yu An; Honghui Du; Dongjie Zhu; Zhaoshuo Tian; Aonghus Lawlor; Ruihai Dong (ICONIP (15), 2023)

Adaptive Adversarial Samples Based Active Learning for Medical Image Classification. - Siteng Ma; Yu An; Jing Wang; Aonghus Lawlor; Ruihai Dong (ICPRAM, 2023)

Keeping People Active and Healthy at Home Using a Reinforcement Learning-based Fitness Recommendation Framework. - Elias Z. Tragos; Diarmuid O'Reilly-Morgan; James Geraci; Bichen Shi; Barry Smyth; Cailbhe Doherty; Aonghus Lawlor; Neil Hurley (IJCAI, 2023)

Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation. - Edoardo D'Amico; Aonghus Lawlor; Neil Hurley (PAKDD (3), 2023)

Scalable Deep Q-Learning for Session-Based Slate Recommendation. - Aayush Singha Roy; Edoardo D'Amico; Elias Z. Tragos; Aonghus Lawlor; Neil Hurley (RecSys, 2023)

Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations. - Ciara Feely; Brian Caulfield 001; Aonghus Lawlor; Barry Smyth (UMAP, 2023)

FewSOME: Few Shot Anomaly Detection. - Niamh Belton; Misgina Tsighe Hagos; Aonghus Lawlor; Kathleen M. Curran (CoRR, 2023)

RecPrompt: A Prompt Tuning Framework for News Recommendation Using Large Language Models. - Dairui Liu; Boming Yang; Honghui Du; Derek Greene; Aonghus Lawlor; Ruihai Dong; Irene Li (CoRR, 2023)

CleftLipGAN : Interactive GAN-Inpainting for Post-Operative Cleft Lip Reconstruction. - Daniel Anojan Atputharuban; Christoph Theopold; Aonghus Lawlor (ACCV Workshops (1), 2024)

Entity Matching with Large Language Models as Weak and Strong Labellers. - Diarmuid O'Reilly-Morgan; Elias Z. Tragos; Erika Duriakova; Honghui Du; Neil Hurley; Aonghus Lawlor (ADBIS (Short Papers), 2024)

RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models. - Dairui Liu; Boming Yang; Honghui Du; Derek Greene; Neil Hurley; Aonghus Lawlor; Ruihai Dong; Irene Li (CIKM, 2024)

ALS Algorithm for Robust and Communication-Efficient Federated Learning. - Neil Hurley; Erika Duriakova; James Geraci; Diarmuid O'Reilly-Morgan; Elias Z. Tragos; Barry Smyth; Aonghus Lawlor (EuroMLSys@EuroSys, 2024)

A Hybrid Decentralised Learning Topology for Recommendations with Improved Privacy. - Diarmuid O'Reilly-Morgan; Elias Z. Tragos; James Geraci; Qinqin Wang; Neil Hurley; Barry Smyth; Aonghus Lawlor (EuroMLSys@EuroSys, 2024)

Breaking the Barrier: Selective Uncertainty-Based Active Learning for Medical Image Segmentation. - Siteng Ma; Haochang Wu; Aonghus Lawlor; Ruihai Dong (ICASSP, 2024)

A Case-Based Reasoning Approach to Post-injury Training Recommendations for Marathon Runners. - Ciara Feely; Brian Caulfield 001; Aonghus Lawlor; Barry Smyth (ICCBR, 2024)

Enhancing Surgical Visualization: Feasibility Study on GAN-Based Image Generation for Post Operative Cleft Palate Images. - Daniel Anojan Atputharuban; Christoph Theopold; Aonghus Lawlor (ICPRAM, 2024)

Simulating Real-World News Consumption: Deep Q-Learning for Diverse User-Centric Slate Recommendations. - Aayush Singha Roy; Elias Z. Tragos; Aonghus Lawlor; Neil Hurley (INRA@RecSys, 2024)

Reconstructing Children's Faces from Cropped Lip and Nose Images: A Large Mask Inpainting Approach for Privacy-Conscious Cleft Data. - Daniel Anojan Atputharuban; Christoph Theopold; Aonghus Lawlor (MICAD, 2024)

Adaptive Curriculum Query Strategy for Active Learning in Medical Image Classification. - Siteng Ma; Honghui Du; Kathleen M. Curran; Aonghus Lawlor; Ruihai Dong (MICCAI (11), 2024)

Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS). - Prateek Mathur; Brendan S. Kelly; Ronan P. Killeen; Aonghus Lawlor (LDTM/MMMI/ML4MHD/ML-CDS@MICCAI, 2024)

Recommending Personalised Targeted Training Adjustments for Marathon Runners. - Ciara Feely; Brian Caulfield 001; Aonghus Lawlor; Barry Smyth (RecSys, 2024)

Exploring Coresets for Efficient Training and Consistent Evaluation of Recommender Systems. - Zheng Ju; Honghui Du; Elias Z. Tragos; Neil Hurley; Aonghus Lawlor (RecSys, 2024)

Using Pseudo Cases and Stratified Case-Based Reasoning to Generate and Evaluate Training Adjustments for Marathon Runners. - Ciara Feely; Brian Caulfield 001; Aonghus Lawlor; Barry Smyth (SGAI Conf. (2), 2024)

Model-data-driven adversarial active learning for brain tumor segmentation. - Siteng Ma; Prateek Mathur; Zheng Ju; Aonghus Lawlor; Ruihai Dong (Comput. Biol. Medicine, 2024)

Towards a unified approach for unsupervised brain MRI Motion Artefact Detection with few shot Anomaly Detection. - Niamh Belton; Misgina Tsighe Hagos; Aonghus Lawlor; Kathleen M. Curran (Comput. Medical Imaging Graph., 2024)

Rethinking Knee Osteoarthritis Severity Grading: A Few Shot Self-Supervised Contrastive Learning Approach. - Niamh Belton; Misgina Tsighe Hagos; Aonghus Lawlor; Kathleen M. Curran (CoRR, 2024)

An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited Data. - Niamh Belton; Aonghus Lawlor; Kathleen M. Curran (CoRR, 2024)

Transformers4NewsRec: A Transformer-based News Recommendation Framework. - Dairui Liu; Honghui Du; Boming Yang; Neil Hurley; Aonghus Lawlor; Irene Li; Derek Greene; Ruihai Dong (CoRR, 2024)

DiffGR: A Discrete Diffusion-Based Model for Personalised Recommendation by Reconstructing User-Item Bipartite Graphs. - Zheng Ju; Honghui Du; Elias Z. Tragos; Neil Hurley; Aonghus Lawlor (ECIR (3), 2025)

NodeRec+: A Lightweight Framework for Federated Recommender Systems. - Diarmuid O'Reilly-Morgan; Erika Duriakova; Elias Z. Tragos; Neil Hurley; Aonghus Lawlor (SIGIR, 2025)

An AI system for continuous knee osteoarthritis severity grading: An anomaly detection inspired approach with few labels. - Niamh Belton; Aonghus Lawlor; Kathleen M. Curran (Artif. Intell. Medicine, 2025)

Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey. - Siteng Ma; Honghui Du; Yu An; Jing Wang; Qinqin Wang; Haochang Wu; Aonghus Lawlor; Ruihai Dong (CoRR, 2025)