Charline Le Lan

According to our database, Charline Le Lan authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Human Alignment of Large Language Models through Online Preference Optimisation.

Daniele Calandriello, Daniel Guo, Rémi Munos, Mark Rowland, Yunhao Tang, Bernardo Ávila Pires, Pierre Harvey Richemond, Charline Le Lan, Michal Valko, Tianqi Liu, Rishabh Joshi, Zeyu Zheng, Bilal Piot

CoRR, 2024

Gemma: Open Models Based on Gemini Research and Technology.

Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Amélie Héliou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Christian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, et al.

CoRR, 2024

2023

Understanding Self-Predictive Learning for Reinforcement Learning.

Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko

Proceedings of the International Conference on Machine Learning, 2023

Bootstrapped Representations in Reinforcement Learning.

Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney

Proceedings of the International Conference on Machine Learning, 2023

Revisiting Bisimulation: A Sampling-Based State Similarity Pseudo-metric.

Charline Le Lan, Rishabh Agarwal

Proceedings of the First Tiny Papers Track at ICLR 2023, 2023

Proto-Value Networks: Scaling Representation Learning with Auxiliary Tasks.

Jesse Farebrother, Joshua Greaves, Rishabh Agarwal, Charline Le Lan, Ross Goroshin, Pablo Samuel Castro, Marc G. Bellemare

Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces.

Charline Le Lan, Joshua Greaves, Jesse Farebrother, Mark Rowland, Fabian Pedregosa, Rishabh Agarwal, Marc G. Bellemare

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

On the Generalization of Representations in Reinforcement Learning.

Charline Le Lan, Stephen Tu, Adam Oberman, Rishabh Agarwal, Marc G. Bellemare

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Perfect Density Models Cannot Guarantee Anomaly Detection.

Charline Le Lan, Laurent Dinh

Entropy, 2021

LieTransformer: Equivariant Self-Attention for Lie Groups.

Michael J. Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim

Proceedings of the 38th International Conference on Machine Learning, 2021

Metrics and Continuity in Reinforcement Learning.

Charline Le Lan, Marc G. Bellemare, Pablo Samuel Castro

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2019

Hierarchical Representations with Poincaré Variational Auto-Encoders.

Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

CoRR, 2019

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders.

Emile Mathieu, Charline Le Lan, Chris J. Maddison, Ryota Tomioka, Yee Whye Teh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019