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Found 12 papers, 8 papers with code
Interactive Hyperparameter Optimization in Multi-Objective Problems via Preference Learning
In an experimental study targeting the environmental impact of ML, we demonstrate that our approach leads to substantially better Pareto fronts compared to optimizing based on a wrong indicator pre-selected by the user, and performs comparable in the case of an advanced user knowing which indicator to pick.
AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks
The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years.
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.
HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection
It is well known that different algorithms perform differently well on an instance of an algorithmic problem, motivating algorithm selection (AS): Given an instance of an algorithmic problem, which is the most suitable algorithm to solve it?
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.
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.
Machine Learning for Online Algorithm Selection under Censored Feedback
In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms.
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.
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.
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.
