However, it is not directly applicable to Reinforcement Learning (RL) due to the inaccessibility of explicit action-value functions.

Multi-task learning is a promising paradigm for handling several tasks simultaneously using a unified architecture.

We study the proportional clustering problem of Chen et al. (ICML '19) and relate

Scene flow estimation is an essential ingredient for a variety of real-world applications, especially for autonomous agents, such as self-driving cars and robots.

In this work, we investigate an alternative setting for tuning regularization parameters, namely data-driven algorithm design, following the previous line of work by Balcan et al. [