48 citations · 114 across the 17 of their papers we have counts for
17 papers
Importance of Smoothness Induced by Optimizers in FL4ASR: Towards Understanding Federated Learning for End-to-End ASR
Sheikh Shams Azam, Tatiana Likhomanenko, Martin Pelikan +1
In this paper, we start by training End-to-End Automatic Speech Recognition (ASR) models using Federated Learning (FL) and examining the fundamental considerations that can be pivo…
Population Expansion for Training Language Models with Private Federated Learning
Tatsuki Koga, Congzheng Song, Martin Pelikan +1
Federated learning (FL) combined with differential privacy (DP) offers machine learning (ML) training with distributed devices and with a formal privacy guarantee. With a large pop…
An Application of a Multivariate Estimation of Distribution Algorithm to Cancer Chemotherapy
Alexander Brownlee, Martin Pelikan, John McCall +1
Chemotherapy treatment for cancer is a complex optimisation problem with a large number of interacting variables and constraints. A number of different probabilistic algorithms hav…
Distance-Based Bias in Model-Directed Optimization of Additively Decomposable Problems
Martin Pelikan, Mark W. Hauschild
For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the var…
Analysis of Evolutionary Algorithms on the One-Dimensional Spin Glass with Power-Law Interactions
Martin Pelikan, Helmut G. Katzgraber
This paper provides an in-depth empirical analysis of several evolutionary algorithms on the one-dimensional spin glass model with power-law interactions. The considered spin glass…
iBOA: The Incremental Bayesian Optimization Algorithm
Martin Pelikan, Kumara Sastry, David E. Goldberg
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the…