Learning to learn and to optimize

From Gradient Descent to Stochastic Gradient Descent (SGD)
RMSProp and Adam
Learning to learn by gradient descent by gradient descent
Learning to optimize

References

[And16L]

Learning to learn by gradient descent by gradient descent, Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W. Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, Nando de Freitas.

Jun 2016

The move from hand-designed features to learned features in machine learning has been wildly successful. In spite of this, optimization algorithms are still designed by hand. In this paper we show how the design of an optimization algorithm can be cast as a learning problem, allowing the algorithm to learn to exploit structure in the problems of interest in an automatic way. Our learned …

Publication

[Li16L]

Learning to Optimize, Ke Li, Jitendra Malik.

Jun 2016

Algorithm design is a laborious process and often requires many iterations of ideation and validation. In this paper, we explore automating algorithm design and present a method to learn an optimization algorithm, which we believe to be the first method that can automatically discover a better algorithm. We approach this problem from a reinforcement learning perspective and represent any …

Publication

[Li17L]

Learning to Optimize Neural Nets, Ke Li, Jitendra Malik.

Mar 2017

Learning to Optimize is a recently proposed framework for learning optimization algorithms using reinforcement learning. In this paper, we explore learning an optimization algorithm for training shallow neural nets. Such high-dimensional stochastic optimization problems present interesting challenges for existing reinforcement learning algorithms. We develop an extension that is suited to learning …

Publication