Alexandre Galashov

According to our database, Alexandre Galashov authored at least 24 papers between 2019 and 2025.

Collaborative distances:

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

2025

Accelerated Diffusion Models via Speculative Sampling.

Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet

CoRR, January, 2025

2024

Deep MMD Gradient Flow without adversarial training.

Alexandre Galashov, Valentin De Bortoli, Arthur Gretton

CoRR, 2024

Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models.

Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias

CoRR, 2024

Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset.

Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Kalman Filter for Online Classification of Non-Stationary Data.

Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research.

Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuan Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de Las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc'Aurelio Ranzato

J. Mach. Learn. Res., 2023

Towards Compute-Optimal Transfer Learning.

Massimo Caccia, Alexandre Galashov, Arthur Douillard, Amal Rannen-Triki, Dushyant Rao, Michela Paganini, Laurent Charlin, Marc'Aurelio Ranzato, Razvan Pascanu

CoRR, 2023

Continually learning representations at scale.

Alexandre Galashov, Jovana Mitrovic, Dhruva Tirumala, Yee Whye Teh, Timothy Nguyen, Arslan Chaudhry, Razvan Pascanu

Proceedings of the Conference on Lifelong Learning Agents, 2023

2022

Behavior Priors for Efficient Reinforcement Learning.

Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess

J. Mach. Learn. Res., 2022

NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research.

Jörg Bornschein, Alexandre Galashov, Ross Hemsley, Amal Rannen-Triki, Yutian Chen, Arslan Chaudhry, Xu Owen He, Arthur Douillard, Massimo Caccia, Qixuang Feng, Jiajun Shen, Sylvestre-Alvise Rebuffi, Kitty Stacpoole, Diego de Las Casas, Will Hawkins, Angeliki Lazaridou, Yee Whye Teh, Andrei A. Rusu, Razvan Pascanu, Marc'Aurelio Ranzato

CoRR, 2022

Data augmentation for efficient learning from parametric experts.

Alexandre Galashov, Joshua Scott Merel, Nicolas Heess

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Game Plan: What AI can do for Football, and What Football can do for AI.

Karl Tuyls, Shayegan Omidshafiei, Paul Muller, Zhe Wang, Jerome T. Connor, Daniel Hennes, Ian Graham, William Spearman, Tim Waskett, Dafydd Steele, Pauline Luc, Adrià Recasens, Alexandre Galashov, Gregory Thornton, Romuald Elie, Pablo Sprechmann, Pol Moreno, Kris Cao, Marta Garnelo, Praneet Dutta, Michal Valko, Nicolas Heess, Alex Bridgland, Julien Pérolat, Bart De Vylder, S. M. Ali Eslami, Mark Rowland, Andrew Jaegle, Rémi Munos, Trevor Back, Razia Ahamed, Simon Bouton, Nathalie Beauguerlange, Jackson Broshear, Thore Graepel, Demis Hassabis

J. Artif. Intell. Res., 2021

Information theoretic meta learning with Gaussian processes.

Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

2020

Learning Dexterous Manipulation from Suboptimal Experts.

Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori

CoRR, 2020

Temporal Difference Uncertainties as a Signal for Exploration.

Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, André Barreto, Razvan Pascanu

CoRR, 2020

Importance Weighted Policy Learning and Adaption.

Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess

CoRR, 2020

Information Theoretic Meta Learning with Gaussian Processes.

Michalis K. Titsias, Sotirios Nikoloutsopoulos, Alexandre Galashov

CoRR, 2020

Learning Dexterous Manipulation from Suboptimal Experts.

Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Alexandre Galashov, Nicolas Heess, Francesco Nori

Proceedings of the 4th Conference on Robot Learning, 2020

2019

Task Agnostic Continual Learning via Meta Learning.

Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu

CoRR, 2019

Meta reinforcement learning as task inference.

Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess

CoRR, 2019

Meta-Learning surrogate models for sequential decision making.

Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, Yee Whye Teh

CoRR, 2019

Exploiting Hierarchy for Learning and Transfer in KL-regularized RL.

Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess

CoRR, 2019

Neural Probabilistic Motor Primitives for Humanoid Control.

Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess

Proceedings of the 7th International Conference on Learning Representations, 2019

Information asymmetry in KL-regularized RL.

Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess

Proceedings of the 7th International Conference on Learning Representations, 2019