# Toward Optimal Run Racing: Application to Deep Learning Calibration

## TL;DR

This paper introduces a method for efficient neural network calibration through early stopping and multiple hypothesis testing, achieving state-of-the-art results without additional hyper-parameters.

## Contribution

It presents a theoretically grounded approach for optimal run selection in deep learning calibration, reducing computational costs and improving performance.

## Key findings

- Significant improvement over existing methods on Cifar10, PTB, and Wiki benchmarks.
- The approach guarantees optimality within a multiple hypothesis testing framework.
- No extra hyper-parameters required for the calibration process.

## Abstract

This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand. The notoriously expensive calibration problem is optimally reduced by detecting and early stopping non-optimal runs. The theoretical contribution regards the optimality guarantees within the multiple hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki benchmarks demonstrate the relevance of the approach with a principled and consistent improvement on the state of the art with no extra hyper-parameter.