Marcus Gallagher

Exchangeability and Kernel Invariance in Trained MLPs

2018 · arXiv (Cornell University)

In the analysis of machine learning models, it is often convenient to assume that the parameters are IID. This assumption is not satisfied when the parameters are updated through training processes such as SGD. A …

Exchangeability and Kernel Invariance in Trained MLPs

2019

In the analysis of machine learning models, it is often convenient to assume that the parameters are IID. This assumption is not satisfied when the parameters are updated through training processes such as Stochastic Gradient …

Pittsburgh learning classifier systems for explainable reinforcement learning

2022 · Proceedings of the Genetic and Evolutionary Computation Conference

Interest in reinforcement learning (RL) has recently surged due to the application of deep learning techniques, but these connectionist approaches are opaque compared with symbolic systems. Learning Classifier Systems (LCSs) are evolutionary machine learning systems …

Analyzing the Runtime of the Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) on the Concatenated Trap Function

2024 · Proceedings of the Genetic and Evolutionary Computation Conference Companion

The Gene-pool Optimal Mixing Evolutionary Algorithm (GOMEA) is a state of the art evolutionary algorithm that leverages linkage learning to efficiently exploit problem structure. By identifying and preserving important building blocks during variation, GOMEA has …