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Books and Theses
- 2020
[b1]Bryan Lim:
Deep learning for time series prediction and decision making over time. University of Oxford, UK, 2020
Journal Articles
- 2024
- [j3]Félix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully:
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. J. Mach. Learn. Res. 25: 108:1-108:16 (2024) - 2023
- [j2]Maxime Allard
, Simón C. Smith, Konstantinos I. Chatzilygeroudis, Bryan Lim, Antoine Cully:
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity. ACM Trans. Evol. Learn. Optim. 3(2): 6:1-6:23 (2023) - [j1]Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully:
Accelerated Quality-Diversity through Massive Parallelism. Trans. Mach. Learn. Res. 2023 (2023)
Conference and Workshop Papers
- 2024
- [c17]Manon Flageat, Bryan Lim, Antoine Cully:
Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms. AAAI 2024: 12024-12032 - [c16]Manon Flageat, Bryan Lim, Antoine Cully:
Enhancing MAP-Elites with Multiple Parallel Evolution Strategies. GECCO 2024 - [c15]Manon Flageat, Bryan Lim, Antoine Cully:
Evolutionary Reinforcement Learning. GECCO Companion 2024: 856-882 - 2023
- [c14]Luca Grillotti, Manon Flageat, Bryan Lim, Antoine Cully:
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. GECCO 2023: 156-164 - [c13]Simón C. Smith, Bryan Lim, Hannah Janmohamed, Antoine Cully:
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning. GECCO Companion 2023: 171-174 - [c12]Bryan Lim, Manon Flageat, Antoine Cully:
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. GECCO 2023: 1212-1220 - [c11]Félix Chalumeau, Raphaël Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot:
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. ICLR 2023 - [c10]Shikha Surana, Bryan Lim, Antoine Cully:
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors. ICRA 2023: 12134-12141 - 2022
- [c9]Bryan Lim, Alexander Reichenbach, Antoine Cully:
Learning to walk autonomously via reset-free quality-diversity. GECCO 2022: 86-94 - [c8]Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully:
QDax: on the benefits of massive parallelization for quality-diversity. GECCO Companion 2022: 128-131 - [c7]Bryan Lim, Luca Grillotti, Lorenzo Bernasconi, Antoine Cully:
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires. ICRA 2022: 5360-5366 - 2020
- [c6]Maria Bauzá Villalonga, Alberto Rodriguez, Bryan Lim, Eric Valls, Theo Sechopoulos:
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering. CoRL 2020: 1015-1029 - [c5]Donghyun Kim, D. Carballo, Jared Di Carlo, Benjamin Katz, Gerardo Bledt, Bryan Lim, Sangbae Kim:
Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot. ICRA 2020: 2464-2470 - [c4]Bryan Lim, Stefan Zohren, Stephen Roberts:
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. IJCNN 2020: 1-8 - [c3]Thomas Dudzik, Matthew Chignoli, Gerardo Bledt, Bryan Lim, Adam Miller, Donghyun Kim, Sangbae Kim:
Robust Autonomous Navigation of a Small-Scale Quadruped Robot in Real-World Environments. IROS 2020: 3664-3671 - 2018
- [c2]Bryan Lim, Mihaela van der Schaar:
Disease-Atlas: Navigating Disease Trajectories using Deep Learning. MLHC 2018: 137-160 - [c1]Bryan Lim:
Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks. NeurIPS 2018: 7494-7504
Informal and Other Publications
- 2024
- [i29]Bryan Lim, Manon Flageat, Antoine Cully:
Large Language Models as In-context AI Generators for Quality-Diversity. CoRR abs/2404.15794 (2024) - [i28]Manon Flageat, Hannah Janmohamed, Bryan Lim, Antoine Cully:
Exploring the Performance-Reproducibility Trade-off in Quality-Diversity. CoRR abs/2409.13315 (2024) - 2023
- [i27]Manon Flageat, Bryan Lim, Antoine Cully:
Multiple Hands Make Light Work: Enhancing Quality and Diversity using MAP-Elites with Multiple Parallel Evolution Strategies. CoRR abs/2303.06137 (2023) - [i26]Bryan Lim, Manon Flageat, Antoine Cully:
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. CoRR abs/2303.06164 (2023) - [i25]Luca Grillotti, Manon Flageat, Bryan Lim, Antoine Cully:
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. CoRR abs/2304.03672 (2023) - [i24]Simón C. Smith, Bryan Lim, Hannah Janmohamed, Antoine Cully:
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning. CoRR abs/2304.12080 (2023) - [i23]Félix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Arthur Flajolet, Thomas Pierrot, Antoine Cully:
QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. CoRR abs/2308.03665 (2023) - [i22]Garðar Ingvarsson, Mikayel Samvelyan, Bryan Lim, Manon Flageat, Antoine Cully, Tim Rocktäschel:
Mix-ME: Quality-Diversity for Multi-Agent Learning. CoRR abs/2311.01829 (2023) - [i21]Manon Flageat, Bryan Lim, Antoine Cully:
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms. CoRR abs/2312.07178 (2023) - 2022
- [i20]Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully:
Accelerated Quality-Diversity for Robotics through Massive Parallelism. CoRR abs/2202.01258 (2022) - [i19]Bryan Lim, Alexander Reichenbach, Antoine Cully:
Learning to Walk Autonomously via Reset-Free Quality-Diversity. CoRR abs/2204.03655 (2022) - [i18]Félix Chalumeau, Raphaël Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot:
Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. CoRR abs/2210.03516 (2022) - [i17]Shikha Surana, Bryan Lim, Antoine Cully:
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors. CoRR abs/2210.04819 (2022) - [i16]Maxime Allard, Simón C. Smith, Konstantinos I. Chatzilygeroudis, Bryan Lim, Antoine Cully:
Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity. CoRR abs/2210.09918 (2022) - [i15]Manon Flageat, Bryan Lim, Luca Grillotti, Maxime Allard, Simón C. Smith, Antoine Cully:
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning. CoRR abs/2211.02193 (2022) - [i14]Bryan Lim, Manon Flageat, Antoine Cully:
Efficient Exploration using Model-Based Quality-Diversity with Gradients. CoRR abs/2211.12610 (2022) - 2021
- [i13]Zihao Zhang, Bryan Lim, Stefan Zohren:
Deep Learning for Market by Order Data. CoRR abs/2102.08811 (2021) - [i12]Daniel Poh, Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures. CoRR abs/2105.10019 (2021) - [i11]Bryan Lim, Luca Grillotti, Lorenzo Bernasconi, Antoine Cully:
Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires. CoRR abs/2109.08522 (2021) - 2020
- [i10]Bryan Lim, Stefan Zohren, Stephen Roberts:
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio. CoRR abs/2002.02008 (2020) - [i9]Bryan Lim, Stefan Zohren:
Time Series Forecasting With Deep Learning: A Survey. CoRR abs/2004.13408 (2020) - [i8]Maria Bauzá, Eric Valls, Bryan Lim, Theo Sechopoulos, Alberto Rodriguez:
Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering. CoRR abs/2012.05205 (2020) - [i7]Daniel Poh, Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Building Cross-Sectional Systematic Strategies By Learning to Rank. CoRR abs/2012.07149 (2020) - 2019
- [i6]Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. CoRR abs/1901.08096 (2019) - [i5]Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Enhancing Time Series Momentum Strategies Using Deep Neural Networks. CoRR abs/1904.04912 (2019) - [i4]Bryan Lim, Stefan Zohren, Stephen J. Roberts:
Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs. CoRR abs/1905.09691 (2019) - [i3]Bryan Lim, Sercan Ömer Arik, Nicolas Loeff, Tomas Pfister:
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. CoRR abs/1912.09363 (2019) - 2018
- [i2]Bryan Lim, Mihaela van der Schaar:
Disease-Atlas: Navigating Disease Trajectories with Deep Learning. CoRR abs/1803.10254 (2018) - [i1]Bryan Lim, Mihaela van der Schaar:
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning. CoRR abs/1807.03159 (2018)
Coauthor Index
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