Gazeau, Maxime

7 publications

NeurIPS 2023 A*Net: A Scalable Path-Based Reasoning Approach for Knowledge Graphs Zhaocheng Zhu, Xinyu Yuan, Michael Galkin, Louis-Pascal Xhonneux, Ming Zhang, Maxime Gazeau, Jian Tang
ICLR 2023 In-Context Reinforcement Learning with Algorithm Distillation Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, Dj Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih
NeurIPSW 2023 Vision-Language Models as a Source of Rewards Kate Baumli, Satinder Singh, Feryal Behbahani, Harris Chan, Gheorghe Comanici, Sebastian Flennerhag, Maxime Gazeau, Kristian Holsheimer, Dan Horgan, Michael Laskin, Clare Lyle, Volodymyr Mnih, Alexander Neitz, Fabio Pardo, Jack Parker-Holder, John Quan, Tim Rocktäschel, Himanshu Sahni, Tom Schaul, Yannick Schroecker, Stephen Spencer, Richie Steigerwald, Luyu Wang, Lei M Zhang
NeurIPSW 2022 In-Context Reinforcement Learning with Algorithm Distillation Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, Dj Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih
NeurIPSW 2022 In-Context Reinforcement Learning with Algorithm Distillation Michael Laskin, Luyu Wang, Junhyuk Oh, Emilio Parisotto, Stephen Spencer, Richie Steigerwald, Dj Strouse, Steven Stenberg Hansen, Angelos Filos, Ethan Brooks, Maxime Gazeau, Himanshu Sahni, Satinder Singh, Volodymyr Mnih
JMLR 2020 A General System of Differential Equations to Model First-Order Adaptive Algorithms Andre Belotto da Silva, Maxime Gazeau
AISTATS 2020 An Empirical Study of Stochastic Gradient Descent with Structured Covariance Noise Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba