297 citations · 838 across the 13 of their papers we have counts for
3 papers · 1 filter
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces
Charline Le Lan, Joshua Greaves, Jesse Farebrother +4
Many machine learning problems encode their data as a matrix with a possibly very large number of rows and columns. In several applications like neuroscience, image compression or…
Understanding Self-Predictive Learning for Reinforcement Learning
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond +13
We study the learning dynamics of self-predictive learning for reinforcement learning, a family of algorithms that learn representations by minimizing the prediction error of their…
On the Generalization of Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Adam Oberman +2
In reinforcement learning, state representations are used to tractably deal with large problem spaces. State representations serve both to approximate the value function with few p…