Improving Algorithm-Selectors and Performance-Predictors via Learning Discriminating Training Samples
The choice of input-data used to train algorithm-selection models is recognised as being a
critical part of the model success. Recently, feature-free methods for algorithm-selection that …
critical part of the model success. Recently, feature-free methods for algorithm-selection that …
On the Utility of Probing Trajectories for Algorithm-Selection
Abstract Machine-learning approaches to algorithm-selection typically take data describing
an instance as input. Input data can take the form of features derived from the instance …
an instance as input. Input data can take the form of features derived from the instance …
Random Filter Map**s as Optimization Problem Feature Extractors
Characterizing optimization problems and their properties addresses a key challenge in
optimization and is crucial for tasks such as creating benchmarks, selecting algorithms, and …
optimization and is crucial for tasks such as creating benchmarks, selecting algorithms, and …
Tackling Threatening behavior through a Semantic Approach
We introduce a new approach to characterize and detect threatening behaviors in
surveillance systems, without relying on history or expertise. This approach consists in …
surveillance systems, without relying on history or expertise. This approach consists in …