96 citations · 328 across the 30 of their papers we have counts for
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A Framework for Population-Based Stochastic Optimization on Abstract Riemannian Manifolds
Robert Simon Fong, Peter Tino
We present Extended Riemannian Stochastic Derivative-Free Optimization (Extended RSDFO), a novel population-based stochastic optimization algorithm on Riemannian manifolds that add…
Dynamical Systems as Temporal Feature Spaces
Peter Tino
Parameterized state space models in the form of recurrent networks are often used in machine learning to learn from data streams exhibiting temporal dependencies. To break the blac…
Foreword to the Focus Issue on Machine Learning in Astronomy and Astrophysics
Giuseppe Longo, Erzsébet Merényi, Peter Tino
Astronomical observations already produce vast amounts of data through a new generation of telescopes that cannot be analyzed manually. Next-generation telescopes such as the Large…
Unmodelled Clustering Methods for Gravitational Wave Populations of Compact Binary Mergers
Jade Powell, Simon Stevenson, Ilya Mandel +1
The mass and spin distributions of compact binary gravitational-wave sources are currently uncertain due to complicated astrophysics involved in their formation. Multiple sub-popul…
Exploiting Synthetically Generated Data with Semi-Supervised Learning for Small and Imbalanced Datasets
Maria Perez-Ortiz, Peter Tino, Rafal Mantiuk +1
Data augmentation is rapidly gaining attention in machine learning. Synthetic data can be generated by simple transformations or through the data distribution. In the latter case,…
A mixture of experts model for predicting persistent weather patterns
Maria Perez-Ortiz, Pedro A. Gutierrez, Peter Tino +2
Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system w…
Feature Relevance Bounds for Ordinal Regression
Lukas Pfannschmidt, Jonathan Jakob, Michael Biehl +2
The increasing occurrence of ordinal data, mainly sociodemographic, led to a renewed research interest in ordinal regression, i.e. the prediction of ordered classes. Besides model…