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Found 4 papers, 1 papers with code
TransOpt: Transformer-based Representation Learning for Optimization Problem Classification
We propose a representation of optimization problem instances using a transformer-based neural network architecture trained for the task of problem classification of the 24 problem classes from the Black-box Optimization Benchmarking (BBOB) benchmark.
Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach
This paper showcases two real-world use cases (home appliances classification and airport shuttle demand prediction) where a hierarchical model applied in the context of zero-inflated data leads to excellent results.
DynamoRep: Trajectory-Based Population Dynamics for Classification of Black-box Optimization Problems
The application of machine learning (ML) models to the analysis of optimization algorithms requires the representation of optimization problems using numerical features.
Less is more: Selecting the right benchmarking set of data for time series classification
By observing discrepancy between the empirical results of the bootstrap evaluation and recently adapted practices in TSC literature when introducing novel methods we warn on the potentially harmful effects of tuning the methods on certain parts of the landscape (unless this is an explicit and desired goal of the study).
