Alexandre Galashov

38 papers A* 6A 1Misc 1Journal 27Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	A*	`conf`	Accelerated Diffusion Models via Speculative Sampling. ICML Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet
2025	J	`jnl`	Accelerated Diffusion Models via Speculative Sampling. CoRR Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet
2025	J	`jnl`	Catch Your Breath: Adaptive Computation for Self-Paced Sequence Production. CoRR Alexandre Galashov, Matt Jones, Nan Rosemary Ke, Yuan Cao, Vaishnavh Nagarajan, Michael C. Mozer
2025	J	`jnl`	Closed-Form Last Layer Optimization. CoRR Alexandre Galashov, Nathaël Da Costa, Liyuan Xu, Philipp Hennig, Arthur Gretton
2025	A*	`conf`	Deep MMD Gradient Flow without adversarial training. ICLR Alexandre Galashov, Valentin De Bortoli, Arthur Gretton
2025	A*	`conf`	Distributional Diffusion Models with Scoring Rules. ICML Valentin De Bortoli, Alexandre Galashov, J. Swaroop Guntupalli, Guangyao Zhou, Kevin Patrick Murphy, Arthur Gretton, Arnaud Doucet
2025	J	`jnl`	Distributional Diffusion Models with Scoring Rules. CoRR Valentin De Bortoli, Alexandre Galashov, J. Swaroop Guntupalli, Guangyao Zhou, Kevin Murphy, Arthur Gretton, Arnaud Doucet
2025	J	`jnl`	Learn to Guide Your Diffusion Model. CoRR Alexandre Galashov, Ashwini Pokle, Arnaud Doucet, Arthur Gretton, Mauricio Delbracio, Valentin De Bortoli
2024	J	`jnl`	Deep MMD Gradient Flow without adversarial training. CoRR Alexandre Galashov, Valentin De Bortoli, Arthur Gretton
2024	A*	`conf`	Kalman Filter for Online Classification of Non-Stationary Data. ICLR Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
2024	A*	`conf`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. NeurIPS Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	J	`jnl`	Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. CoRR Alexandre Galashov, Michalis K. Titsias, András György, Clare Lyle, Razvan Pascanu, Yee Whye Teh, Maneesh Sahani
2024	J	`jnl`	Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. CoRR Amal Rannen-Triki, Jörg Bornschein, Razvan Pascanu, Marcus Hutter, András György, Alexandre Galashov, Yee Whye Teh, Michalis K. Titsias
2023	—	`conf`	Continually learning representations at scale. CoLLAs Alexandre Galashov, Jovana Mitrovic, Dhruva Tirumala, Yee Whye Teh, Timothy Nguyen, Arslan Chaudhry, Razvan Pascanu
2023	J	`jnl`	Kalman Filter for Online Classification of Non-Stationary Data. CoRR Michalis K. Titsias, Alexandre Galashov, Amal Rannen-Triki, Razvan Pascanu, Yee Whye Teh, Jörg Bornschein
2023	J	`jnl`	Nevis'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research. J. Mach. Learn. Res. 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
2023	J	`jnl`	Towards Compute-Optimal Transfer Learning. CoRR Massimo Caccia, Alexandre Galashov, Arthur Douillard, Amal Rannen-Triki, Dushyant Rao, Michela Paganini, Laurent Charlin, Marc'Aurelio Ranzato, Razvan Pascanu
2022	J	`jnl`	Behavior Priors for Efficient Reinforcement Learning. J. Mach. Learn. Res. Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess
2022	A*	`conf`	Data augmentation for efficient learning from parametric experts. NeurIPS Alexandre Galashov, Joshua Scott Merel, Nicolas Heess
2022	J	`jnl`	Data augmentation for efficient learning from parametric experts. CoRR Alexandre Galashov, Josh Merel, Nicolas Heess
2022	J	`jnl`	NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision Research. CoRR 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
2021	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. J. Artif. Intell. Res. 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
2021	A	`conf`	Information theoretic meta learning with Gaussian processes. UAI Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov
2020	J	`jnl`	Behavior Priors for Efficient Reinforcement Learning. CoRR Dhruva Tirumala, Alexandre Galashov, Hyeonwoo Noh, Leonard Hasenclever, Razvan Pascanu, Jonathan Schwarz, Guillaume Desjardins, Wojciech Marian Czarnecki, Arun Ahuja, Yee Whye Teh, Nicolas Heess
2020	J	`jnl`	Game Plan: What AI can do for Football, and What Football can do for AI. CoRR 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
2020	J	`jnl`	Importance Weighted Policy Learning and Adaption. CoRR Alexandre Galashov, Jakub Sygnowski, Guillaume Desjardins, Jan Humplik, Leonard Hasenclever, Rae Jeong, Yee Whye Teh, Nicolas Heess
2020	J	`jnl`	Information Theoretic Meta Learning with Gaussian Processes. CoRR Michalis K. Titsias, Sotirios Nikoloutsopoulos, Alexandre Galashov
2020	Misc	`conf`	Learning Dexterous Manipulation from Suboptimal Experts. CoRL Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Alexandre Galashov, Nicolas Heess, Francesco Nori
2020	J	`jnl`	Learning Dexterous Manipulation from Suboptimal Experts. CoRR Rae Jeong, Jost Tobias Springenberg, Jackie Kay, Daniel Zheng, Yuxiang Zhou, Alexandre Galashov, Nicolas Heess, Francesco Nori
2020	J	`jnl`	Temporal Difference Uncertainties as a Signal for Exploration. CoRR Sebastian Flennerhag, Jane X. Wang, Pablo Sprechmann, Francesco Visin, Alexandre Galashov, Steven Kapturowski, Diana L. Borsa, Nicolas Heess, André Barreto, Razvan Pascanu
2019	J	`jnl`	Exploiting Hierarchy for Learning and Transfer in KL-regularized RL. CoRR Dhruva Tirumala, Hyeonwoo Noh, Alexandre Galashov, Leonard Hasenclever, Arun Ahuja, Greg Wayne, Razvan Pascanu, Yee Whye Teh, Nicolas Heess
2019	—	`conf`	Information asymmetry in KL-regularized RL. ICLR (Poster) Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess
2019	J	`jnl`	Information asymmetry in KL-regularized RL. CoRR Alexandre Galashov, Siddhant M. Jayakumar, Leonard Hasenclever, Dhruva Tirumala, Jonathan Schwarz, Guillaume Desjardins, Wojciech M. Czarnecki, Yee Whye Teh, Razvan Pascanu, Nicolas Heess
2019	J	`jnl`	Meta reinforcement learning as task inference. CoRR Jan Humplik, Alexandre Galashov, Leonard Hasenclever, Pedro A. Ortega, Yee Whye Teh, Nicolas Heess
2019	J	`jnl`	Meta-Learning surrogate models for sequential decision making. CoRR Alexandre Galashov, Jonathan Schwarz, Hyunjik Kim, Marta Garnelo, David Saxton, Pushmeet Kohli, S. M. Ali Eslami, Yee Whye Teh
2019	—	`conf`	Neural Probabilistic Motor Primitives for Humanoid Control. ICLR (Poster) Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess
2019	J	`jnl`	Task Agnostic Continual Learning via Meta Learning. CoRR Xu He, Jakub Sygnowski, Alexandre Galashov, Andrei A. Rusu, Yee Whye Teh, Razvan Pascanu
2018	J	`jnl`	Neural probabilistic motor primitives for humanoid control. CoRR Josh Merel, Leonard Hasenclever, Alexandre Galashov, Arun Ahuja, Vu Pham, Greg Wayne, Yee Whye Teh, Nicolas Heess