Results 21 to 30 of about 6,098 (213)
CoRR, 2021 We propose the ChaCha (Champion-Challengers) algorithm for making an online choice of hyperparameters in online learning settings. ChaCha handles the process of determining a champion and scheduling a set of `live' challengers over time based on sample complexity bounds. It is guaranteed to have sublinear regret after the optimal configuration is added
Qingyun Wu +4 more
openaire +4 more sources
AutoML in heavily constrained applications
The VLDB Journal, 2023 AbstractOptimizing a machine learning pipeline for a task at hand requires careful configuration of various hyperparameters, typically supported by an AutoML system that optimizes the hyperparameters for the given training dataset. Yet, depending on the AutoML system’s own second-order meta-configuration, the performance of the AutoML process can vary ...
Felix Neutatz +2 more
openaire +5 more sources
owenodriscoll/AutoML: first public release
, 2023 What's Changed Dev setup by @owenodriscoll in https://github.com/owenodriscoll/AutoML/pull/15 Automl dev by @owenodriscoll in https://github.com/owenodriscoll/AutoML/pull/16 Full Changelog: https://github.com/owenodriscoll/AutoML/compare/v2.0...v2.
owenodriscoll, nenanen
core +1 more source
, 2022 An automated machine learning-assisted ensemble framework (AutoML-Ens) for assembling multiple models in geosciences.
Hao Chen (11770646)
core +1 more source
An Ontology-Based Concept for Meta AutoML
, 2021 Part 4: Automated Machine LearningInternational audienceAutomated machine learning (AutoML) supports ML engineers and data scientists by automating tasks like model selection and hyperparameter optimization.
Bernhard G. Humm +3 more
core +1 more source
AutoML for Advanced Monitoring in Digital Manufacturing and Industry 4.0 [PDF]
, 2023 openThe emergence of Industry 4.0 and the associated rise of sensing technologies in industrial equipment has made the adoption of Machine Learning (ML) solutions crucial in enhancing and making more efficient enterprise production processes.
PERATONER, ALESSANDRO
core
The Potential of AutoML for Demand Forecasting
Proceedings of the Conference on Production Systems and Logistics, 2022 In demand forecasting, which can depend on various internal and external factors, machine learning (ML) methods can capture complex patterns and enable precise forecasts.
Kramer, Kathrin Julia +4 more
doaj +1 more source
CoRR, 2019 Contextual Bandits is one of the widely popular techniques used in applications such as personalization, recommendation systems, mobile health, causal marketing etc . As a dynamic approach, it can be more efficient than standard A/B testing in minimizing regret.
Praneet Dutta +3 more
openaire +2 more sources
DREAM: Debugging and Repairing AutoML Pipelines [PDF]
, 2023 Deep Learning models have become an integrated component of modern software systems. In response to the challenge of model design, researchers proposed Automated Machine Learning (AutoML) systems, which automatically search for model architecture and ...
Zhai, Juan +3 more
core +1 more source
CoRR, 2021 This work improves the quality of automated machine learning (AutoML) systems by using dataset and function descriptions while significantly decreasing computation time from minutes to milliseconds by using a zero-shot approach. Given a new dataset and a well-defined machine learning task, humans begin by reading a description of the dataset and ...
Nikhil Singh 0003 +5 more
openaire +2 more sources