Robert Pinsler

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José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
Gregor N. C. SimmMicrosoft ResearchVerified email at microsoft.com
Daniel ZügnerMicrosoft ResearchVerified email at microsoft.com
Jonathan GordonOpenAIVerified email at openai.com
Eric NalisnickAssistant Professor, Johns Hopkins UniversityVerified email at jhu.edu
Claudio ZeniSenior Researcher @ MicrosoftVerified email at microsoft.com
Tian XieMicrosoft ResearchVerified email at microsoft.com
Matthew HortonLawrence Berkeley National Lab, University of California BerkeleyVerified email at lbl.gov
Lixin Sun 孙励新Microsoft Research, Cambridge, UKVerified email at microsoft.com
Gerhard NeumannProfessor, Karlsruhe Institute of Technology (KIT)Verified email at robot-learning.de
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeVerified email at cam.ac.uk
Marloes ArtsGenmabVerified email at di.ku.dk
Victor Garcia SatorrasUniversity of AmsterdamVerified email at uva.nl
Chin-Wei HuangMicrosoft ResearchVerified email at microsoft.com
Rianne van den BergPrincipal Research Manager, Microsoft ResearchVerified email at microsoft.com
Cecilia ClementiProfessor of Physics, Freie Universität BerlinVerified email at fu-berlin.de
Noe FrankMicrosoft ResearchVerified email at microsoft.com
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com

Robert Pinsler

Microsoft Research

Verified email at microsoft.com - Homepage

AI for Science Reinforcement Learning Active Learning


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Bayesian batch active learning as sparse subset approximation R Pinsler, J Gordon, E Nalisnick, JM Hernández-Lobato Advances in Neural Information Processing Systems, 6359-6370, 2019	134	2019
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics GNC Simm, R Pinsler, JM Hernández-Lobato International Conference on Machine Learning, 8959-8969, 2020	113	2020
Symmetry-Aware Actor-Critic for 3D Molecular Design GNC Simm, R Pinsler, G Csányi, JM Hernández-Lobato International Conference on Learning Representations, 2021	65	2021
Two for one: Diffusion models and force fields for coarse-grained molecular dynamics M Arts, V Garcia Satorras, CW Huang, D Zugner, M Federici, C Clementi, ... Journal of Chemical Theory and Computation 19 (18), 6151-6159, 2023	53	2023
MatterGen: a generative model for inorganic materials design C Zeni, R Pinsler, D Zügner, A Fowler, M Horton, X Fu, S Shysheya, ... arXiv preprint arXiv:2312.03687, 2023	38	2023
Sample and feedback efficient hierarchical reinforcement learning from human preferences R Pinsler, R Akrour, T Osa, J Peters, G Neumann 2018 IEEE international conference on robotics and automation (ICRA), 596-601, 2018	27	2018
Inverse Reinforcement Learning of Bird Flocking Behavior R Pinsler, M Maag, O Arenz, G Neumann ICRA 2018 Workshop on Swarms: From Biology to Robotics and Back, 2018	9	2018
Factored Contextual Policy Search with Bayesian Optimization R Pinsler, P Karkus, A Kupcsik, D Hsu, WS Lee 2019 IEEE International Conference on Robotics and Automation (ICRA), 7242-7248, 2019	5	2019
Mattersim: A deep learning atomistic model across elements, temperatures and pressures H Yang, C Hu, Y Zhou, X Liu, Y Shi, J Li, G Li, Z Chen, S Chen, C Zeni, ... arXiv preprint arXiv:2405.04967, 2024	2	2024
Advances in Active Learning and Sequential Decision Making R Pinsler University of Cambridge, 2022	1	2022

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