Alexandre Galashov

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 27

Top Topics

Gaussian Process

Temporal Difference

American Football

Reinforcement Learning

Top Venues

J. Mach. Learn. Res.

J. Artif. Intell. Res.

Publications

Alexandre Galashov, Valentin De Bortoli, Arthur Gretton
Deep MMD Gradient Flow without adversarial training. CoRR (2024)
Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
Kalman Filter for Online Classification of Non-Stationary Data. ICLR (2024)
Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. CoRR (2024)
Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
Kalman Filter for Online Classification of Non-Stationary Data. CoRR (2023)
Massimo Caccia, Alexandre Galashov, Arthur Douillard, Amal Rannen-Triki, Dushyant Rao, Michela Paganini, Laurent Charlin, Marc'Aurelio Ranzato, Razvan Pascanu
Towards Compute-Optimal Transfer Learning. CoRR (2023)
Alexandre Galashov, Jovana Mitrovic, Dhruva Tirumala, Yee Whye Teh, Timothy Nguyen, Arslan Chaudhry, Razvan Pascanu
Continually learning representations at scale. CoLLAs (2023)
Alexandre Galashov, Joshua Scott Merel, Nicolas Heess
Data augmentation for efficient learning from parametric experts. NeurIPS (2022)
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
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research. CoRR (2022)
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess
Behavior Priors for Efficient Reinforcement Learning. J. Mach. Learn. Res. 23 (2022)
Alexandre Galashov, Josh Merel, Nicolas Heess
Data augmentation for efficient learning from parametric experts. CoRR (2022)
Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov
Information theoretic meta learning with Gaussian processes. UAI (2021)
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
Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 71 (2021)
Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori
Learning Dexterous Manipulation from Suboptimal Experts. CoRR (2020)
Michalis K. Titsias, Sotirios Nikoloutsopoulos, Alexandre Galashov
Information Theoretic Meta Learning with Gaussian Processes. CoRR (2020)
Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess
Behavior Priors for Efficient Reinforcement Learning. CoRR (2020)
Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, André Barreto, Razvan Pascanu
Temporal Difference Uncertainties as a Signal for Exploration. CoRR (2020)
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
Game Plan: What AI can do for Football, and What Football can do for AI. CoRR (2020)
Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess
Importance Weighted Policy Learning and Adaption. CoRR (2020)
Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Alexandre Galashov, Nicolas Heess, Francesco Nori
Learning Dexterous Manipulation from Suboptimal Experts. CoRL (2020)
Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, Yee Whye Teh
Meta-Learning surrogate models for sequential decision making. CoRR (2019)
Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess
Information asymmetry in KL-regularized RL. CoRR (2019)
Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess
Information asymmetry in KL-regularized RL. ICLR (Poster) (2019)
Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess
Meta reinforcement learning as task inference. CoRR (2019)
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess
Neural Probabilistic Motor Primitives for Humanoid Control. ICLR (Poster) (2019)
Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess
Exploiting Hierarchy for Learning and Transfer in KL-regularized RL. CoRR (2019)
Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu
Task Agnostic Continual Learning via Meta Learning. CoRR (2019)
Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess
Neural probabilistic motor primitives for humanoid control. CoRR (2018)