Deep learning (DL) has celebrated many successes, but it’s still a challenge to find the right model for a given dataset — especially with a limited budget. Adapting DL models to new problems can be computationally intensive and requires comprehensive training data. On tabular data, AutoML solutions like Auto-SkLearn and AutoGluon work very well. However, […]
Learning Synthetic Environments and Reward Networks for Reinforcement Learning
In supervised learning, multiple works have investigated training networks using artificial data. For instance, in dataset distillation, the information of a larger dataset is distilled into a smaller synthetic dataset in order to improve train time. Synthetic environments (SEs) aim to apply a similar idea to Reinforcement learning (RL). They are proxies for real environments […]