# Multi-Task Multicriteria Hyperparameter Optimization

Source: [Multi-Task Multicriteria Hyperparameter Optimization](<https://www.sota2.com/research/paper/68fa67051e209131cea13d28>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:04:19.938Z

Authors: Kirill Akhmetzyanov, Alexander Yuzhakov

Published: 2020-02-15T12:47:53.000Z

## Abstract

We present a new method for searching optimal hyperparameters among several tasks and several criteria. Multi-Task Multi Criteria method (MTMC) provides several Pareto-optimal solutions, among which one solution is selected with given criteria significance coefficients. The article begins with a mathematical formulation of the problem of choosing optimal hyperparameters. Then, the steps of the MTMC method that solves this problem are described. The proposed method is evaluated on the image classification problem using a convolutional neural network. The article presents optimal hyperparameters for various criteria significance coefficients.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2002.06372>)

[PDF](<https://arxiv.org/pdf/2002.06372.pdf>)