4 papers
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure
Tyler Sam, Yudong Chen, Christina Lee Yu
Many reinforcement learning (RL) algorithms are too costly to use in practice due to the large sizes of the problem's state and action space. To resolve this issue, we study…
Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure
Tyler Sam, Yudong Chen, Christina Lee Yu
The practicality of reinforcement learning algorithms has been limited due to poor scaling with respect to the problem size, as the sample complexity of learning an -optimal pol…
Limits of Transfer Learning
Jake Williams, Abel Tadesse, Tyler Sam +2
Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer lea…
Decomposable Probability-of-Success Metrics in Algorithmic Search
Tyler Sam, Jake Williams, Abel Tadesse +2
Previous studies have used a specific success metric within an algorithmic search framework to prove machine learning impossibility results. However, this specific success metric p…