Charline Le Lan

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Emile MathieuPostdoctoral Research Associate, University of CambridgeVerified email at cam.ac.uk
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMindVerified email at stats.ox.ac.uk
Marc G. BellemareGoogle BrainVerified email at google.com
Rishabh AgarwalSenior Research Scientist, Google BrainVerified email at google.com
Chris J. MaddisonUniversity of TorontoVerified email at cs.toronto.edu
Ryota TomiokaMicrosoft Research CambridgeVerified email at microsoft.com
Pablo Samuel CastroGoogleVerified email at google.com
Emilien DupontResearch Scientist, DeepMindVerified email at stats.ox.ac.uk
Hyunjik KimResearch Scientist, DeepMindVerified email at google.com
Laurent DinhAppleVerified email at apple.com
Stephen TuGoogleVerified email at google.com
Mark RowlandResearch Scientist, Google DeepMindVerified email at google.com
Joshua GreavesGoogle BrainVerified email at google.com
Jesse FarebrotherMila / McGill University; Google DeepMindVerified email at cs.mcgill.ca
Will DabneyDeepMindVerified email at google.com
Fabian PedregosaGoogleVerified email at google.com
Ross GoroshinGoogle BrainVerified email at google.com
Anna HarutyunyanDeepMindVerified email at google.com

Charline Le Lan

PhD. University of Oxford

Verified email at stats.ox.ac.uk

Reinforcement Learning Deep Learning Machine Learning Artificial Intelligence


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Continuous Hierarchical Representations with Poincar\'e Variational Auto-Encoders E Mathieu, C Le Lan, CJ Maddison, R Tomioka, YW Teh Advances in Neural Information Processing Systems (NeurIPS19), 2019	164*	2019
Lietransformer: Equivariant self-attention for lie groups MJ Hutchinson, C Le Lan, S Zaidi, E Dupont, YW Teh, H Kim International Conference on Machine Learning (ICML21), equal contribution, 2021	76*	2021
Perfect density models cannot guarantee anomaly detection C Le Lan, L Dinh Journal Entropy, ICBINB NeurIPS20 Workshop (Entropic Award,Oral); arXiv …, 2021	36	2021
Metrics and continuity in reinforcement learning C Le Lan, MG Bellemare, PS Castro AAAI Conference on Artificial Intelligence 35, (AAAI21), 2021	17	2021
On the Generalization of Representations in Reinforcement Learning C Le Lan, S Tu, A Oberman, R Agarwal, MG Bellemare International Conference on Artificial Intelligence and Statistics (AISTATS22), 2022	13	2022
Understanding Self-Predictive Learning for Reinforcement Learning Y Tang, ZD Guo, PH Richemond, BÁ Pires, Y Chandak, R Munos, ... International Conference on Machine Learning (ICML23), 2023	4*	2023
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces C Le Lan, J Greaves, J Farebrother, M Rowland, F Pedregosa, R Agarwal, ... International Conference on Artificial Intelligence and Statistics (AISTATS23), 2023	3	2023
Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks J Farebrother, J Greaves, R Agarwal, C Le Lan, R Goroshin, PS Castro, ... International Conference on Learning Representations (ICLR23), *equal …, 2023	2	2023
Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric C Le Lan, R Agarwal ICLR 2023 Tiny Papers, 2023	1	2023
Bootstrapped Representations in Reinforcement Learning C Le Lan, S Tu, M Rowland, A Harutyunyan, R Agarwal, MG Bellemare, ... International Conference on Machine Learning (ICML23), 2023		2023

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