5 citations · 18 across the 14 of their papers we have counts for
6 papers · 1 filter
Modified Adaptive Tree-Structured Parzen Estimator for Hyperparameter Optimization
Szymon Sieradzki, Jacek Mańdziuk
In this paper, we review hyperparameter optimization methods for machine learning models, with a particular focus on the Adaptive Tree-Structured Parzen Estimator (ATPE) algorithm.…
Cultivating Archipelago of Forests: Evolving Robust Decision Trees through Island Coevolution
Adam Żychowski, Andrew Perrault, Jacek Mańdziuk
Decision trees are widely used in machine learning due to their simplicity and interpretability, but they often lack robustness to adversarial attacks and data perturbations. The p…
Bootstrap Sampling Rate Greater than 1.0 May Improve Random Forest Performance
Stanisław Kaźmierczak, Jacek Mańdziuk
Random forests (RFs) utilize bootstrap sampling to generate individual training sets for each component tree by sampling with replacement, with the sample size typically equal to t…
Interpretable Multi-task Learning with Shared Variable Embeddings
Maciej Żelaszczyk, Jacek Mańdziuk
This paper proposes a general interpretable predictive system with shared information. The system is able to perform predictions in a multi-task setting where distinct tasks are no…
ReModels: Quantile Regression Averaging models
Grzegorz Zakrzewski, Kacper Skonieczka, Mikołaj Małkiński +1
Electricity price forecasts play a crucial role in making key business decisions within the electricity markets. A focal point in this domain are probabilistic predictions, which d…
Coevolutionary Algorithm for Building Robust Decision Trees under Minimax Regret
Adam Żychowski, Andrew Perrault, Jacek Mańdziuk
In recent years, there has been growing interest in developing robust machine learning (ML) models that can withstand adversarial attacks, including one of the most widely adopted,…