92 citations · 270 across the 13 of their papers we have counts for
4 papers · 1 filter
Scalable Gaussian Process Inference with Stan
Till Hoffmann, Jukka-Pekka Onnela
Gaussian processes (GPs) are sophisticated distributions to model functional data. Whilst theoretically appealing, they are computationally cumbersome except for small datasets. We…
Unifying Summary Statistic Selection for Approximate Bayesian Computation
Till Hoffmann, Jukka-Pekka Onnela
Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize three different classes of summaries and d…
Cost-based feature selection for network model choice
Louis Raynal, Till Hoffmann, Jukka-Pekka Onnela
Selecting a small set of informative features from a large number of possibly noisy candidates is a challenging problem with many applications in machine learning and approximate B…
Unified treatment of the asymptotics of asymmetric kernel density estimators
Till Hoffmann, Nick S. Jones
We extend balloon and sample-smoothing estimators, two types of variable-bandwidth kernel density estimators, by a shift parameter and derive their asymptotic properties. Our appro…