  We propose a novel white-box approach to hyper-parameter optimization.
Motivated by recent work establishing a relationship between flat minima and
generalization, we first establish a relationship between the strong convexity
of the loss and its flatness. Based on this, we seek to find hyper-parameter
configurations that improve flatness by minimizing the strong convexity of the
loss. By using the structure of the underlying neural network, we derive
closed-form equations to approximate the strong convexity parameter, and
attempt to find hyper-parameters that minimize it in a randomized fashion.
Through experiments on 14 classification datasets, we show that our method
achieves strong performance at a fraction of the runtime.
