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Found 9 papers, 3 papers with code
counterfactuals: An R Package for Counterfactual Explanation Methods
Counterfactual explanation methods provide information on how feature values of individual observations must be changed to obtain a desired prediction.
Multi-Objective Hyperparameter Optimization -- An Overview
Hyperparameter optimization constitutes a large part of typical modern machine learning workflows.
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.
Self-GenomeNet: Self-supervised Learning with Reverse-Complement Context Prediction for Nucleotide-level Genomics Data
We introduce Self-GenomeNet, a novel contrastive self-supervised learning method for nucleotide-level genomic data, which substantially improves the quality of the learned representations and performance compared to the current state-of-the-art deep 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.
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.
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.
Multi-Objective Counterfactual Explanations
We show the usefulness of MOC in concrete cases and compare our approach with state-of-the-art methods for counterfactual explanations.
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.
