Bryan Lim - RankMe

56 papers A* 6A 5B 1Misc 1Journal 36Unranked 6

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Exploring the Performance-Reproducibility Trade-Off in Quality-Diversity. IEEE Trans. Evol. Comput. Manon Flageat, Hannah Janmohamed, Bryan Lim, Antoine Cully
2025	J	`jnl`	EXPLICATE: Enhancing Phishing Detection through Explainable AI and LLM-Powered Interpretability. CoRR Bryan Lim, Roman Huerta, Alejandro Sotelo, Anthonie Quintela, Priyanka Kumar
2025	—	`conf`	Evolutionary Reinforcement Learning. GECCO Companion Antoine Cully, Bryan Lim, Paul Templier, Manon Flageat
2025	J	`jnl`	Graph of Agents: Principled Long Context Modeling by Emergent Multi-Agent Collaboration. CoRR Taejong Joo, Shu Ishida, Ivan Sosnovik, Bryan Lim, Sahand Rezaei-Shoshtari, Adam Gaier, Robert Giaquinto
2025	—	`conf`	Narrative-Based Interactive Learning for Scam Prevention: Rich Within Reach. CSEDU (1) Weile Tu, Bryan Lim, Victor W. Ong, Juay Hee Tan, Ashe Xy Lee, Peisen Xu, Anand Bhojan
2025	J	`jnl`	The Temporal Evolution of Large Language Model Performance: A Comparative Analysis of Past and Current Outputs in Scientific and Medical Research. Informatics Ishith Seth, Gianluca Marcaccini, Bryan Lim, Jennifer Novo, Stephen Bacchi, Roberto Cuomo, Richard J. Ross, Warren M. Rozen
2024	A*	`conf`	Beyond Expected Return: Accounting for Policy Reproducibility When Evaluating Reinforcement Learning Algorithms. AAAI Manon Flageat, Bryan Lim, Antoine Cully
2024	A	`conf`	Enhancing MAP-Elites with Multiple Parallel Evolution Strategies. GECCO Manon Flageat, Bryan Lim, Antoine Cully
2024	—	`conf`	Evolutionary Reinforcement Learning. GECCO Companion Manon Flageat, Bryan Lim, Antoine Cully
2024	J	`jnl`	Exploring the Performance-Reproducibility Trade-off in Quality-Diversity. CoRR Manon Flageat, Hannah Janmohamed, Bryan Lim, Antoine Cully
2024	J	`jnl`	Large Language Models as In-context AI Generators for Quality-Diversity. CoRR Bryan Lim, Manon Flageat, Antoine Cully
2024	J	`jnl`	QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. J. Mach. Learn. Res. Félix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Guillaume Richard, Arthur Flajolet, Thomas Pierrot, Antoine Cully
2023	J	`jnl`	Accelerated Quality-Diversity through Massive Parallelism. Trans. Mach. Learn. Res. Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully
2023	J	`jnl`	Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms. CoRR Manon Flageat, Bryan Lim, Antoine Cully
2023	A	`conf`	Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. GECCO Luca Grillotti, Manon Flageat, Bryan Lim, Antoine Cully
2023	J	`jnl`	Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains. CoRR Luca Grillotti, Manon Flageat, Bryan Lim, Antoine Cully
2023	A*	`conf`	Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors. ICRA Shikha Surana, Bryan Lim, Antoine Cully
2023	J	`jnl`	Mix-ME: Quality-Diversity for Multi-Agent Learning. CoRR Garðar Ingvarsson, Mikayel Samvelyan, Bryan Lim, Manon Flageat, Antoine Cully, Tim Rocktäschel
2023	J	`jnl`	Multiple Hands Make Light Work: Enhancing Quality and Diversity using MAP-Elites with Multiple Parallel Evolution Strategies. CoRR Manon Flageat, Bryan Lim, Antoine Cully
2023	A*	`conf`	Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. ICLR Félix Chalumeau, Raphaël Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot
2023	J	`jnl`	Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity. ACM Trans. Evol. Learn. Optim. Maxime Allard, Simón C. Smith, Konstantinos I. Chatzilygeroudis, Bryan Lim, Antoine Cully
2023	J	`jnl`	QDax: A Library for Quality-Diversity and Population-based Algorithms with Hardware Acceleration. CoRR Félix Chalumeau, Bryan Lim, Raphaël Boige, Maxime Allard, Luca Grillotti, Manon Flageat, Valentin Macé, Arthur Flajolet, Thomas Pierrot, Antoine Cully
2023	—	`conf`	Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning. GECCO Companion Simón C. Smith, Bryan Lim, Hannah Janmohamed, Antoine Cully
2023	J	`jnl`	Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning. CoRR Simón C. Smith, Bryan Lim, Hannah Janmohamed, Antoine Cully
2023	A	`conf`	Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. GECCO Bryan Lim, Manon Flageat, Antoine Cully
2023	J	`jnl`	Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning. CoRR Bryan Lim, Manon Flageat, Antoine Cully
2022	J	`jnl`	Accelerated Quality-Diversity for Robotics through Massive Parallelism. CoRR Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully
2022	J	`jnl`	Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning. CoRR Manon Flageat, Bryan Lim, Luca Grillotti, Maxime Allard, Simón C. Smith, Antoine Cully
2022	A*	`conf`	Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires. ICRA Bryan Lim, Luca Grillotti, Lorenzo Bernasconi, Antoine Cully
2022	J	`jnl`	Efficient Exploration using Model-Based Quality-Diversity with Gradients. CoRR Bryan Lim, Manon Flageat, Antoine Cully
2022	J	`jnl`	Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors. CoRR Shikha Surana, Bryan Lim, Antoine Cully
2022	J	`jnl`	Learning to Walk Autonomously via Reset-Free Quality-Diversity. CoRR Bryan Lim, Alexander Reichenbach, Antoine Cully
2022	A	`conf`	Learning to walk autonomously via reset-free quality-diversity. GECCO Bryan Lim, Alexander Reichenbach, Antoine Cully
2022	J	`jnl`	Neuroevolution is a Competitive Alternative to Reinforcement Learning for Skill Discovery. CoRR Félix Chalumeau, Raphaël Boige, Bryan Lim, Valentin Macé, Maxime Allard, Arthur Flajolet, Antoine Cully, Thomas Pierrot
2022	J	`jnl`	Online Damage Recovery for Physical Robots with Hierarchical Quality-Diversity. CoRR Maxime Allard, Simón C. Smith, Konstantinos I. Chatzilygeroudis, Bryan Lim, Antoine Cully
2022	—	`conf`	QDax: on the benefits of massive parallelization for quality-diversity. GECCO Companion Bryan Lim, Maxime Allard, Luca Grillotti, Antoine Cully
2021	J	`jnl`	Deep Learning for Market by Order Data. CoRR Zihao Zhang, Bryan Lim, Stefan Zohren
2021	J	`jnl`	Dynamics-Aware Quality-Diversity for Efficient Learning of Skill Repertoires. CoRR Bryan Lim, Luca Grillotti, Lorenzo Bernasconi, Antoine Cully
2021	J	`jnl`	Enhancing Cross-Sectional Currency Strategies by Ranking Refinement with Transformer-based Architectures. CoRR Daniel Poh, Bryan Lim, Stefan Zohren, Stephen J. Roberts
2020	J	`jnl`	Building Cross-Sectional Systematic Strategies By Learning to Rank. CoRR Daniel Poh, Bryan Lim, Stefan Zohren, Stephen J. Roberts
2020	—	`—`	Deep learning for time series prediction and decision making over time. Bryan Lim
2020	J	`jnl`	Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio. CoRR Bryan Lim, Stefan Zohren, Stephen Roberts
2020	B	`conf`	Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. IJCNN Bryan Lim, Stefan Zohren, Stephen Roberts
2020	A	`conf`	Robust Autonomous Navigation of a Small-Scale Quadruped Robot in Real-World Environments. IROS Thomas Dudzik, Matthew Chignoli, Gerardo Bledt, Bryan Lim, Adam Miller, Donghyun Kim, Sangbae Kim
2020	Misc	`conf`	Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering. CoRL Maria Bauzá Villalonga, Alberto Rodriguez, Bryan Lim, Eric Valls, Theo Sechopoulos
2020	J	`jnl`	Tactile Object Pose Estimation from the First Touch with Geometric Contact Rendering. CoRR Maria Bauzá, Eric Valls, Bryan Lim, Theo Sechopoulos, Alberto Rodriguez
2020	J	`jnl`	Time Series Forecasting With Deep Learning: A Survey. CoRR Bryan Lim, Stefan Zohren
2020	A*	`conf`	Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot. ICRA Donghyun Kim, D. Carballo, Jared Di Carlo, Benjamin Katz, Gerardo Bledt, Bryan Lim, Sangbae Kim
2019	J	`jnl`	Enhancing Time Series Momentum Strategies Using Deep Neural Networks. CoRR Bryan Lim, Stefan Zohren, Stephen J. Roberts
2019	J	`jnl`	Population-based Global Optimisation Methods for Learning Long-term Dependencies with RNNs. CoRR Bryan Lim, Stefan Zohren, Stephen J. Roberts
2019	J	`jnl`	Recurrent Neural Filters: Learning Independent Bayesian Filtering Steps for Time Series Prediction. CoRR Bryan Lim, Stefan Zohren, Stephen J. Roberts
2019	J	`jnl`	Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting. CoRR Bryan Lim, Sercan Ömer Arik, Nicolas Loeff, Tomas Pfister
2018	—	`conf`	Disease-Atlas: Navigating Disease Trajectories using Deep Learning. MLHC Bryan Lim, Mihaela van der Schaar
2018	J	`jnl`	Disease-Atlas: Navigating Disease Trajectories with Deep Learning. CoRR Bryan Lim, Mihaela van der Schaar
2018	J	`jnl`	Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning. CoRR Bryan Lim, Mihaela van der Schaar
2018	A*	`conf`	Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks. NeurIPS Bryan Lim