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Found 17 papers, 12 papers with code
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.
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
We present TabPFN, a trained Transformer that can do supervised classification for small tabular datasets in less than a second, needs no hyperparameter tuning and is competitive with state-of-the-art classification methods.
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Algorithm parameters, in particular hyperparameters of machine learning algorithms, can substantially impact their performance.
HPOBench: A Collection of Reproducible Multi-Fidelity Benchmark Problems for HPO
To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial component of machine learning and its applications.
Squirrel: A Switching Hyperparameter Optimizer
In this short note, we describe our submission to the NeurIPS 2020 BBO challenge.
Neural Model-based Optimization with Right-Censored Observations
When fitting a regression model to predict the distribution of the outcomes, we cannot simply drop these right-censored observations, but need to properly model them.
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
Automated Machine Learning (AutoML) supports practitioners and researchers with the tedious task of designing machine learning pipelines and has recently achieved substantial success.
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters
Bayesian Optimization (BO) is a common approach for hyperparameter optimization (HPO) in automated machine learning.
BOAH: A Tool Suite for Multi-Fidelity Bayesian Optimization & Analysis of Hyperparameters
Hyperparameter optimization and neural architecture search can become prohibitively expensive for regular black-box Bayesian optimization because the training and evaluation of a single model can easily take several hours.
Neural Networks for Predicting Algorithm Runtime Distributions
Many state-of-the-art algorithms for solving hard combinatorial problems in artificial intelligence (AI) include elements of stochasticity that lead to high variations in runtime, even for a fixed problem instance.
Deep learning with convolutional neural networks for decoding and visualization of EEG pathology
We apply convolutional neural networks (ConvNets) to the task of distinguishing pathological from normal EEG recordings in the Temple University Hospital EEG Abnormal Corpus.
Pitfalls and Best Practices in Algorithm Configuration
Good parameter settings are crucial to achieve high performance in many areas of artificial intelligence (AI), such as propositional satisfiability solving, AI planning, scheduling, and machine learning (in particular deep learning).
Efficient Benchmarking of Algorithm Configuration Procedures via Model-Based Surrogates
In our experiments, we construct and evaluate surrogate benchmarks for hyperparameter optimization as well as for AC problems that involve performance optimization of solvers for hard combinatorial problems, drawing training data from the runs of existing AC procedures.
Deep learning with convolutional neural networks for EEG decoding and visualization
PLEASE READ AND CITE THE REVISED VERSION at Human Brain Mapping: http://onlinelibrary. wiley. com/doi/10. 1002/hbm. 23730/full Code available here: https://github. com/robintibor/braindecode
Efficient and Robust Automated Machine Learning
The success of machine learning in a broad range of applications has led to an ever-growing demand for machine learning systems that can be used off the shelf by non-experts.
Supplementary Material for Efficient and Robust Automated Machine Learning
Supplementary Material for Efficient and Robust Automated Machine Learning
