42 citations · 46 across the 5 of their papers we have counts for
5 papers
Designing weights for quartet-based methods when data is heterogeneous across lineages
Marta Casanellas, Jesús Fernández-Sánchez, Marina Garrote-López +1
Homogeneity across lineages is a common assumption in phylogenetics according to which nucleotide substitution rates remain constant in time and do not depend on lineages. This is…
Sig-Wasserstein GANs for Time Series Generation
Hao Ni, Lukasz Szpruch, Marc Sabate-Vidales +3
Synthetic data is an emerging technology that can significantly accelerate the development and deployment of AI machine learning pipelines. In this work, we develop high-fidelity t…
Solving path dependent PDEs with LSTM networks and path signatures
Marc Sabate-Vidales, David Šiška, Lukasz Szpruch
Using a combination of recurrent neural networks and signature methods from the rough paths theory we design efficient algorithms for solving parametric families of path dependent…
Multi-index Antithetic Stochastic Gradient Algorithm
Mateusz B. Majka, Marc Sabate-Vidales, Łukasz Szpruch
Stochastic Gradient Algorithms (SGAs) are ubiquitous in computational statistics, machine learning and optimisation. Recent years have brought an influx of interest in SGAs, and th…
Conditional Sig-Wasserstein GANs for Time Series Generation
Shujian Liao, Hao Ni, Lukasz Szpruch +3
Generative adversarial networks (GANs) have been extremely successful in generating samples, from seemingly high dimensional probability measures. However, these methods struggle t…