Marc Deisenroth (University College London)
Коллоквиум Факультета математики и компьютерных наук
Четверг 3 декабря 18:15 zoom ID 958-115-833
In many high-impact areas of machine learning, we face the challenge of data-efficient learning, i.e., learning from scarce data. This includes healthcare, climate science, and autonomous robots. There are many approaches toward learning from scarce data. In this talk, I will discuss a few of them in the context of reinforcement learning. First, I will motivate probabilistic, model-based approaches to reinforcement learning, which allow us to reduce the effect of model errors. Second, I will discuss a meta-learning approach that allows us to generalize knowledge across tasks to enable few-shot learning.
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