High-impact areas of machine learning and AI, such as personalized healthcare, autonomous robots, or environmental science share some practical challenges: They are either small-data problems or a small collection of big-data problems. Therefore, learning algorithms need to be data/sample efficient, i.e., they need to be able to learn in complex domains, but only from fairly small datasets. Approaches for data-efficient learning include probabilistic modeling and inference, Bayesian deep learning, meta learning, Bayesian optimization, few-shot learning, etc. In this talk, Marc will give a brief overview of some approaches to tackle the data-efficiency challenge. First, he will discuss a data-efficient reinforcement learning algorithm, which highlights the necessity for probabilistic models in RL. He will then present a meta-learning method for generalizing knowledge across tasks. Finally, he will motivate deep Gaussian processes, richer probabilistic models, which are composed of relatively simple building blocks. He will briefly discuss the model, inference and some potential extensions, which can be valuable for modeling complex relationships, while providing some uncertainty estimates, which will be useful in any downstream decision-making process.