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Found 23 papers, 12 papers with code
ALPBench: A Benchmark for Active Learning Pipelines on Tabular Data
This particularly holds for the combination of query strategies with different learning algorithms into active learning pipelines and examining the impact of the learning algorithm choice.
Position: Why We Must Rethink Empirical Research in Machine Learning
We warn against a common but incomplete understanding of empirical research in machine learning that leads to non-replicable results, makes findings unreliable, and threatens to undermine progress in the field.
Automated Machine Learning for Multi-Label Classification
Automated machine learning (AutoML) aims to select and configure machine learning algorithms and combine them into machine learning pipelines tailored to a dataset at hand.
Information Leakage Detection through Approximate Bayes-optimal Prediction
Though effective, emerging supervised machine learning based approaches to detect ILs are limited to binary system sensitive information and lack a comprehensive framework.
Iterative Deepening Hyperband
Hyperparameter optimization (HPO) is concerned with the automated search for the most appropriate hyperparameter configuration (HPC) of a parameterized machine learning algorithm.
PyExperimenter: Easily distribute experiments and track results
PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms and in particular is designed to reduce the involved manual effort significantly.
Meta-Learning for Automated Selection of Anomaly Detectors for Semi-Supervised Datasets
In anomaly detection, a prominent task is to induce a model to identify anomalies learned solely based on normal data.
Hyperparameter optimization in deep multi-target prediction
As a result of the ever increasing complexity of configuring and fine-tuning machine learning models, the field of automated machine learning (AutoML) has emerged over the past decade.
A Survey of Methods for Automated Algorithm Configuration
We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry.
Naive Automated Machine Learning
An essential task of Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on a given dataset.
Towards Green Automated Machine Learning: Status Quo and Future Directions
Therefore, we first elaborate on how to quantify the environmental footprint of an AutoML tool.
Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
The notion of bounded rationality originated from the insight that perfectly rational behavior cannot be realized by agents with limited cognitive or computational resources.
Algorithm Selection on a Meta Level
The problem of selecting an algorithm that appears most suitable for a specific instance of an algorithmic problem class, such as the Boolean satisfiability problem, is called instance-specific algorithm selection.
Replacing the Ex-Def Baseline in AutoML by Naive AutoML
Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset.
Annotation Uncertainty in the Context of Grammatical Change
This paper elaborates on the notion of uncertainty in the context of annotation in large text corpora, specifically focusing on (but not limited to) historical languages.
Naive Automated Machine Learning -- A Late Baseline for AutoML
Automated Machine Learning (AutoML) is the problem of automatically finding the pipeline with the best generalization performance on some given dataset.
Towards Meta-Algorithm Selection
Instance-specific algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidates most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime.
A Flexible Class of Dependence-aware Multi-Label Loss Functions
For evaluating such predictions, the set of predicted labels needs to be compared to the ground-truth label set associated with that instance, and various loss functions have been proposed for this purpose.
Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction
Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a key requirement for both linguistic research and subsequent automated natural language processing (NLP) tasks.
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis
In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
Extreme Algorithm Selection With Dyadic Feature Representation
Algorithm selection (AS) deals with selecting an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem, e. g., choosing solvers for SAT problems.
Automated Multi-Label Classification based on ML-Plan
Automated machine learning (AutoML) has received increasing attention in the recent past.
ML-Plan: Automated machine learning via hierarchical planning
Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset).
