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 (ICLR (Poster), 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 (ICLR (Poster), 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)

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)

Meta reinforcement learning as task inference. - Jan Humplik; Alexandre Galashov; Leonard Hasenclever; Pedro A. Ortega; Yee Whye Teh; Nicolas Heess (CoRR, 2019)

Task Agnostic Continual Learning via Meta Learning. - Xu He; Jakub Sygnowski; Alexandre Galashov; Andrei A. Rusu; Yee Whye Teh; Razvan Pascanu (CoRR, 2019)

Learning Dexterous Manipulation from Suboptimal Experts. - Rae Jeong; Jost Tobias Springenberg; Jackie Kay; Daniel Zheng; Alexandre Galashov; Nicolas Heess; Francesco Nori (CoRL, 2020)

Information Theoretic Meta Learning with Gaussian Processes. - Michalis K. Titsias; Sotirios Nikoloutsopoulos; Alexandre Galashov (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)

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 001; Razvan Pascanu (CoRR, 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)

Information theoretic meta learning with Gaussian processes. - Michalis K. Titsias; Francisco J. R. Ruiz; Sotirios Nikoloutsopoulos; Alexandre Galashov (UAI, 2021)

Game Plan: What AI can do for Football, and What Football can do for AI. - Karl Tuyls; Shayegan Omidshafiei; Paul Muller; Zhe Wang 055; Jerome T. Connor; Daniel Hennes; Ian Graham; William Spearman; Tim Waskett; Dafydd Steele; Pauline Luc; Adrià Recasens; et al. (J. Artif. Intell. Res., 2021)

Data augmentation for efficient learning from parametric experts. - Alexandre Galashov; Joshua Scott Merel; Nicolas Heess (NeurIPS, 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 001; Arslan Chaudhry; Xu Owen He; Arthur Douillard; Massimo Caccia; Qixuang Feng; Jiajun Shen; Sylvestre-Alvise Rebuffi; et al. (CoRR, 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)

Continually learning representations at scale. - Alexandre Galashov; Jovana Mitrovic; Dhruva Tirumala; Yee Whye Teh; Timothy Nguyen; Arslan Chaudhry; Razvan Pascanu (CoLLAs, 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)

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 001; Arslan Chaudhry; Xu Owen He; Arthur Douillard; Massimo Caccia; Qixuan Feng; Jiajun Shen; Sylvestre-Alvise Rebuffi; et al. (J. Mach. Learn. Res., 2023)

Kalman Filter for Online Classification of Non-Stationary Data. - Michalis K. Titsias; Alexandre Galashov; Amal Rannen-Triki; Razvan Pascanu; Yee Whye Teh; Jörg Bornschein (ICLR, 2024)

Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. - Alexandre Galashov; Michalis K. Titsias; András György 001; Clare Lyle; Razvan Pascanu; Yee Whye Teh; Maneesh Sahani (NeurIPS, 2024)

Revisiting Dynamic Evaluation: Online Adaptation for Large Language Models. - Amal Rannen-Triki; Jörg Bornschein; Razvan Pascanu; Marcus Hutter; András György 001; Alexandre Galashov; Yee Whye Teh; Michalis K. Titsias (CoRR, 2024)

Deep MMD Gradient Flow without adversarial training. - Alexandre Galashov; Valentin De Bortoli; Arthur Gretton (ICLR, 2025)

Accelerated Diffusion Models via Speculative Sampling. - Valentin De Bortoli; Alexandre Galashov; Arthur Gretton; Arnaud Doucet (CoRR, 2025)

Distributional Diffusion Models with Scoring Rules. - Valentin De Bortoli; Alexandre Galashov; J. Swaroop Guntupalli; Guangyao Zhou; Kevin Murphy 002; Arthur Gretton; Arnaud Doucet (CoRR, 2025)