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Found 22 papers, 12 papers with code
One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions
For each of these universes, we compute metrics of fairness and performance.
Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML
The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices.
Mind the Gap: Measuring Generalization Performance Across Multiple Objectives
Modern machine learning models are often constructed taking into account multiple objectives, e. g., minimizing inference time while also maximizing accuracy.
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.
A geometric framework for outlier detection in high-dimensional data
Outlier or anomaly detection is an important task in data analysis.
Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
Flexible Group Fairness Metrics for Survival Analysis
In this paper we explore how to utilise existing survival metrics to measure bias with group fairness metrics.
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.
Automated Benchmark-Driven Design and Explanation of Hyperparameter Optimizers
Automated hyperparameter optimization (HPO) has gained great popularity and is an important ingredient of most automated machine learning frameworks.
YAHPO Gym -- An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization
When developing and analyzing new hyperparameter optimization methods, it is vital to empirically evaluate and compare them on well-curated benchmark suites.
Mutation is all you need
Neural architecture search (NAS) promises to make deep learning accessible to non-experts by automating architecture engineering of deep neural networks.
Meta-Learning for Symbolic Hyperparameter Defaults
Hyperparameter optimization in machine learning (ML) deals with the problem of empirically learning an optimal algorithm configuration from data, usually formulated as a black-box optimization problem.
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of additive regression models and deep networks.
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.
Debiasing classifiers: is reality at variance with expectation?
We present an empirical study of debiasing methods for classifiers, showing that debiasers often fail in practice to generalize out-of-sample, and can in fact make fairness worse rather than better.
Neural Mixture Distributional Regression
We present neural mixture distributional regression (NMDR), a holistic framework to estimate complex finite mixtures of distributional regressions defined by flexible additive predictors.
Benchmarking time series classification -- Functional data vs machine learning approaches
In order to assess the methods and implementations, we run a benchmark on a wide variety of representative (time series) data sets, with in-depth analysis of empirical results, and strive to provide a reference ranking for which method(s) to use for non-expert practitioners.
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.
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
To carry out a clinical research under this scenario, an analyst could train a machine learning model only on local data site, but it is still possible to execute a statistical query at a certain cost in the form of sending a machine learning model to some of the remote data sites and get the performance measures as feedback, maybe due to prediction being usually much cheaper.
Learning Multiple Defaults for Machine Learning Algorithms
The performance of modern machine learning methods highly depends on their hyperparameter configurations.
mlr Tutorial
This document provides and in-depth introduction to the mlr framework for machine learning experiments in R.
