# How to Automatically Tune Machine Learning Settings Using Smart Math

> A system that uses advanced statistical modeling to automatically find the best settings for complex machine learning models, saving engineers from manual trial and error.

- **Patent:** US 11501192
- **Original title:** Systems and methods for Bayesian optimization using non-linear mapping of input
- **Owner:** University of Toronto
- **Granted:** 2022
- **Status:** Active
- **Times cited:** 0
- **Field:** ai_ml, software, consumer_electronics

## What it does

This patent describes a way to automate the tuning of machine learning systems, specifically by finding the best hyper-parameters. Hyper-parameters are the configuration settings, like learning rates or layer sizes, that define how a model learns. The system uses a probabilistic model—a mathematical way of representing uncertainty—to predict which settings will yield the best performance. The core innovation is applying a non-linear mapping to these settings before the model processes them, which helps the system better understand complex, non-obvious relationships between settings and outcomes. By iteratively evaluating the system and updating the model, it converges on the optimal configuration.

## What it does NOT cover

- Does not cover manual tuning of machine learning models by human engineers.
- Does not cover optimization methods that rely solely on linear transformations.
- Does not cover the actual architecture of the neural network being tuned, only the process of selecting its hyper-parameters.
- Does not cover non-probabilistic search methods like simple grid search or random search.

## The clever bit

By using a non-linear one-to-one mapping to transform the input domain, the system can model complex, warped landscapes of hyper-parameters that traditional Gaussian processes might struggle to represent accurately.

## Real-world examples

1. AutoML platforms for neural network architecture search
2. Hyper-parameter optimization frameworks for deep learning
3. Automated model training pipelines in cloud AI services

## Why it matters

Tuning machine learning models is notoriously time-consuming and expensive, often requiring massive computing resources. By automating this process, this technology enables faster development cycles and better-performing AI models. It represents a shift toward 'AutoML,' where the infrastructure itself learns how to optimize its own configuration.

## Frequently asked questions

### What does How to Automatically Tune Machine Learning Settings Using Smart Math cover?

A system that uses advanced statistical modeling to automatically find the best settings for complex machine learning models, saving engineers from manual trial and error.

### Who owns patent US 11501192?

University of Toronto owns this patent, granted in 2022.

### When does this patent expire?

This patent is expected to expire on November 15, 2042, when the invention enters the public domain.

### What problem does this patent solve?

Tuning machine learning models is notoriously time-consuming and expensive, often requiring massive computing resources. By automating this process, this technology enables faster development cycles and better-performing AI models. It represents a shift toward 'AutoML,' where the infrastructure itself learns how to optimize its own configuration.

### What does this patent NOT cover?

Does not cover manual tuning of machine learning models by human engineers.

**Full plain-English explainer:** https://patentbrief.org/patent/us/11501192/alphafold-protein-structure-prediction

**Original patent:** https://patents.google.com/patent/US11501192

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_Source: PatentBrief — https://patentbrief.org. Patent facts are from public records; the plain-English explanation is PatentBrief's._