Search Results for author:
Found 20 papers, 9 papers with code
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models
Efficient optimization is achieved via augmentation of the search space of the learning algorithm by incorporating feature selection, interaction and monotonicity constraints into the hyperparameter search space.
MO-DEHB: Evolutionary-based Hyperband for Multi-Objective Optimization
Hyperparameter optimization (HPO) is a powerful technique for automating the tuning of machine learning (ML) models.
Tackling Neural Architecture Search With Quality Diversity Optimization
Although considerable progress has been made in the field of multi-objective NAS, we argue that there is some discrepancy between the actual optimization problem of practical interest and the optimization problem that multi-objective NAS tries to solve.
AMLB: an AutoML Benchmark
Comparing different AutoML frameworks is notoriously challenging and often done incorrectly.
Multi-Objective Hyperparameter Optimization -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models
The goal of Quality Diversity Optimization is to generate a collection of diverse yet high-performing solutions to a given problem at hand.
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges
Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance.
Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features
Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis.
Deep Semi-Supervised Learning for Time Series Classification
Based on these adaptations, we explore the potential of deep semi-supervised learning in the context of time series classification by evaluating our methods on large public time series classification problems with varying amounts of labelled samples.
Multi-Objective Hyperparameter Tuning and Feature Selection using Filter Ensembles
While model-based optimization needs fewer objective evaluations to achieve good performance, it incurs computational overhead compared to the NSGA-II, so the preferred choice depends on the cost of evaluating a model on given data.
Towards Human Centered AutoML
Building models from data is an integral part of the majority of data science workflows.
Multi-Objective Automatic Machine Learning with AutoxgboostMC
AutoML systems are currently rising in popularity, as they can build powerful models without human oversight.
An Open Source AutoML Benchmark
In recent years, an active field of research has developed around automated machine learning (AutoML).
Wearable-based Parkinson's Disease Severity Monitoring using Deep Learning
To solve the problem of limited availability of high quality training data, we propose a transfer learning technique which helps to improve model performance substantially.
Automatic Gradient Boosting
Automatic machine learning performs predictive modeling with high performing machine learning tools without human interference.
Automatic Exploration of Machine Learning Experiments on OpenML
Understanding the influence of hyperparameters on the performance of a machine learning algorithm is an important scientific topic in itself and can help to improve automatic hyperparameter tuning procedures.
mlrMBO: A Modular Framework for Model-Based Optimization of Expensive Black-Box Functions
We present mlrMBO, a flexible and comprehensive R toolbox for model-based optimization (MBO), also known as Bayesian optimization, which addresses the problem of expensive black-box optimization by approximating the given objective function through a surrogate regression model.
Probing for sparse and fast variable selection with model-based boosting
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables.
Stability selection for component-wise gradient boosting in multiple dimensions
We apply this new algorithm to a study to estimate abundance of common eider in Massachusetts, USA, featuring excess zeros, overdispersion, non-linearity and spatio-temporal structures.
mlr Tutorial
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.
