Disease-Atlas: Navigating Disease Trajectories using Deep Learning. - Bryan Lim; Mihaela van der Schaar (MLHC, 2018)

Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks. - Bryan Lim (NeurIPS, 2018)

Disease-Atlas: Navigating Disease Trajectories with Deep Learning. - Bryan Lim; Mihaela van der Schaar (CoRR, 2018)

Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning. - Bryan Lim; Mihaela van der Schaar (CoRR, 2018)

Enhancing Time Series Momentum Strategies Using Deep Neural Networks. - Bryan Lim; Stefan Zohren; Stephen J. Roberts (CoRR, 2019)

Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs. - Bryan Lim; Stefan Zohren; Stephen J. Roberts (CoRR, 2019)

Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. - Bryan Lim; Sercan Ömer Arik; Nicolas Loeff; Tomas Pfister (CoRR, 2019)

Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering. - Maria Bauzá Villalonga; Alberto Rodriguez 003; Bryan Lim; Eric Valls; Theo Sechopoulos (CoRL, 2020)

Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot. - Donghyun Kim 002; D. Carballo; Jared Di Carlo; Benjamin Katz; Gerardo Bledt; Bryan Lim; Sangbae Kim (ICRA, 2020)

Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. - Bryan Lim; Stefan Zohren; Stephen Roberts 001 (IJCNN, 2020)

Robust Autonomous Navigation of a Small-Scale Quadruped Robot in Real-World Environments. - Thomas Dudzik; Matthew Chignoli; Gerardo Bledt; Bryan Lim; Adam Miller; Donghyun Kim 002; Sangbae Kim (IROS, 2020)

Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio. - Bryan Lim; Stefan Zohren; Stephen Roberts 001 (CoRR, 2020)

Time Series Forecasting With Deep Learning: A Survey. - Bryan Lim; Stefan Zohren (CoRR, 2020)

Building Cross-Sectional Systematic Strategies By Learning to Rank. - Daniel Poh; Bryan Lim; Stefan Zohren; Stephen J. Roberts (CoRR, 2020)

Deep learning for time series prediction and decision making over time. - Bryan Lim (2020)

Deep Learning for Market by Order Data. - Zihao Zhang; Bryan Lim; Stefan Zohren (CoRR, 2021)

Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures. - Daniel Poh; Bryan Lim; Stefan Zohren; Stephen J. Roberts (CoRR, 2021)

QDax: on the benefits of massive parallelization for quality-diversity. - Bryan Lim; Maxime Allard; Luca Grillotti; Antoine Cully (GECCO Companion, 2022)

Learning to walk autonomously via reset-free quality-diversity. - Bryan Lim; Alexander Reichenbach; Antoine Cully (GECCO, 2022)

Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires. - Bryan Lim; Luca Grillotti; Lorenzo Bernasconi; Antoine Cully (ICRA, 2022)

Accelerated Quality-Diversity for Robotics through Massive Parallelism. - Bryan Lim; Maxime Allard; Luca Grillotti; Antoine Cully (CoRR, 2022)

Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning. - Manon Flageat; Bryan Lim; Luca Grillotti; Maxime Allard; Simón C. Smith; Antoine Cully (CoRR, 2022)

Efficient Exploration using Model-Based Quality-Diversity with Gradients. - Bryan Lim; Manon Flageat; Antoine Cully (CoRR, 2022)

Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. - Luca Grillotti; Manon Flageat; Bryan Lim; Antoine Cully (GECCO, 2023)

Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. - Bryan Lim; Manon Flageat; Antoine Cully (GECCO, 2023)

Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning. - Simón C. Smith; Bryan Lim; Hannah Janmohamed; Antoine Cully (GECCO Companion, 2023)

Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. - Félix Chalumeau; Raphaël Boige; Bryan Lim; Valentin Macé; Maxime Allard; Arthur Flajolet; Antoine Cully; Thomas Pierrot (ICLR, 2023)

Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors. - Shikha Surana; Bryan Lim; Antoine Cully (ICRA, 2023)

Multiple Hands Make Light Work: Enhancing Quality and Diversity using MAP-Elites with Multiple Parallel Evolution Strategies. - Manon Flageat; Bryan Lim; Antoine Cully (CoRR, 2023)

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. - Félix Chalumeau; Bryan Lim; Raphaël Boige; Maxime Allard; Luca Grillotti; Manon Flageat; Valentin Macé; Arthur Flajolet; Thomas Pierrot; Antoine Cully (CoRR, 2023)

Mix-ME: Quality-Diversity for Multi-Agent Learning. - Garðar Ingvarsson; Mikayel Samvelyan; Bryan Lim; Manon Flageat; Antoine Cully; Tim Rocktäschel (CoRR, 2023)

Accelerated Quality-Diversity through Massive Parallelism. - Bryan Lim; Maxime Allard; Luca Grillotti; Antoine Cully (Trans. Mach. Learn. Res., 2023)

Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity. - Maxime Allard; Simón C. Smith; Konstantinos I. Chatzilygeroudis; Bryan Lim; Antoine Cully (ACM Trans. Evol. Learn. Optim., 2023)

Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms. - Manon Flageat; Bryan Lim; Antoine Cully (AAAI, 2024)

Enhancing MAP-Elites with Multiple Parallel Evolution Strategies. - Manon Flageat; Bryan Lim; Antoine Cully (GECCO, 2024)

Evolutionary Reinforcement Learning. - Manon Flageat; Bryan Lim; Antoine Cully (GECCO Companion, 2024)

Large Language Models as In-context AI Generators for Quality-Diversity. - Bryan Lim; Manon Flageat; Antoine Cully (CoRR, 2024)

Exploring the Performance-Reproducibility Trade-off in Quality-Diversity. - Manon Flageat; Hannah Janmohamed; Bryan Lim; Antoine Cully (CoRR, 2024)

QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. - Félix Chalumeau; Bryan Lim; Raphaël Boige; Maxime Allard; Luca Grillotti; Manon Flageat; Valentin Macé; Guillaume Richard; Arthur Flajolet; Thomas Pierrot; Antoine Cully (J. Mach. Learn. Res., 2024)

Narrative-Based Interactive Learning for Scam Prevention: Rich Within Reach. - Weile Tu; Bryan Lim; Victor W. Ong; Juay Hee Tan; Ashe Xy Lee; Peisen Xu; Anand Bhojan (CSEDU (1), 2025)

EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability. - Bryan Lim; Roman Huerta; Alejandro Sotelo; Anthonie Quintela; Priyanka Kumar (CoRR, 2025)