Zero-Shot Selection of Pretrained Models

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, […]

Read More

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 […]

Read More