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 …
an algorithmic problem class, such as the Boolean satisfiability problem, is called instance …
Run2Survive: A decision-theoretic approach to algorithm selection based on survival analysis
Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of
candidate algorithms most suitable for a specific instance of an algorithmic problem class …
candidate algorithms most suitable for a specific instance of an algorithmic problem class …
Extreme algorithm selection with dyadic feature representation
Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of
candidate algorithms most suitable for a specific instance of an algorithmic problem class …
candidate algorithms most suitable for a specific instance of an algorithmic problem class …
Hybrid ranking and regression for algorithm selection
Algorithm selection (AS) is defined as the task of automatically selecting the most suitable
algorithm from a set of candidate algorithms for a specific instance of an algorithmic problem …
algorithm from a set of candidate algorithms for a specific instance of an algorithmic problem …
A recommender system for scientific datasets and analysis pipelines
Scientific datasets and analysis pipelines are increasingly being shared publicly in the
interest of open science. However, mechanisms are lacking to reliably identify which …
interest of open science. However, mechanisms are lacking to reliably identify which …
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 …
to an agent one after another, and the agent has to quickly select a presumably best …
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. Research on …
cannot be realized by agents with limited cognitive or computational resources. Research on …
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 …
from a fixed set of candidates most suitable for a specific instance of an algorithmic problem …
[PDF][PDF] Algorithm selection on a meta level
T Alexander, G Lukas, T Tanja - 2023 - dlib.phenikaa-uni.edu.vn
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 …
an algorithmic problem class, such as the Boolean satisfiability problem, is called instance …
A Robust Bayesian Estimation Approach for the Imprecise Plackett–Luce Model
Learning to rank has become an important part in the fields of machine learning and
statistical learning. Rankings are indeed present in many applications, including cognitive …
statistical learning. Rankings are indeed present in many applications, including cognitive …