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Found 10 papers, 4 papers with code
Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis
We argue that interpretations of machine learning (ML) models or the model-building process can bee seen as a form of sensitivity analysis (SA), a general methodology used to explain complex systems in many fields such as environmental modeling, engineering, or economics.
RAISE -- Radiology AI Safety, an End-to-end lifecycle approach
The integration of AI into radiology introduces opportunities for improved clinical care provision and efficiency but it demands a meticulous approach to mitigate potential risks as with any other new technology.
Multi-Objective Hyperparameter Optimization -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution
Despite all the benefits of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves.
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.
Explaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models.
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.
Towards Explaining Hyperparameter Optimization via Partial Dependence Plots
Automated hyperparameter optimization (HPO) can support practitioners to obtain peak performance in machine learning models.
Train, Learn, Expand, Repeat
High-quality labeled data is essential to successfully train supervised machine learning models.
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.
