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20102024
most citedA Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

2.1k citations · 9.2k across the 77 of their papers we have counts for

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Showing 2016 · stat.MLShow all

2 papers · 2 filters

stat.ML2016★ 174 cited

Learning to Learn without Gradient Descent by Gradient Descent

Yutian Chen, Matthew W. Hoffman, Sergio Gomez Colmenarejo +4

We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfe…

stat.ML2016★ 56 cited

Learning to Perform Physics Experiments via Deep Reinforcement Learning

Misha Denil, Pulkit Agrawal, Tejas D Kulkarni +3

When encountering novel objects, humans are able to infer a wide range of physical properties such as mass, friction and deformability by interacting with them in a goal driven way…