2 citations · 2 across the 1 of their papers we have counts for
4 papers · 1 filter
Expert-Aided Causal Discovery of Ancestral Graphs
Tiago da Silva, Bruna Bazaluk, Eliezer de Souza da Silva +6
Causal discovery (CD) is an important component of many scientific applications, yet most techniques produce unreliable point estimates that often contradict expert knowledge. To m…
Non-separable Spatio-temporal Graph Kernels via SPDEs
Alexander Nikitin, ST John, Arno Solin +1
Gaussian processes (GPs) provide a principled and direct approach for inference and learning on graphs. However, the lack of justified graph kernels for spatio-temporal modelling h…
TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series
Alexander Nikitin, Letizia Iannucci, Samuel Kaski
Temporally indexed data are essential in a wide range of fields and of interest to machine learning researchers. Time series data, however, are often scarce or highly sensitive, wh…
Learning Relevant Contextual Variables Within Bayesian Optimization
Julien Martinelli, Ayush Bharti, Armi Tiihonen +5
Contextual Bayesian Optimization (CBO) efficiently optimizes black-box functions with respect to design variables, while simultaneously integrating contextual information regarding…