7 citations · 7 across the 4 of their papers we have counts for
5 papers
IQS-BO: In-Context Query Selection for Bayesian Optimisation
Luca Geminiani, Nadja Klein
Bayesian Optimisation (BO) is a powerful framework for the optimisation of expensive black-box functions, but typically requires refitting a surrogate and maximising an acquisition…
Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
Philipp Dahlinger, Balázs Gyenes, Niklas Freymuth +6
Graph Network Simulators (GNSs) have emerged as powerful surrogates for complex physics-based simulation, offering inherent differentiability and orders-of-magnitude speedups over…
Diffusion-based Denoising Beats Vanilla Score Matching in Parameter Estimation: A Theoretical Explanation
Benedikt Lütke Schwienhorst, Nadja Klein, Johannes Lederer
Score matching is an alternative to maximum likelihood estimation when the normalizing constant is unknown or too costly to evaluate. However, vanilla score matching has shown to b…
Scalable and Robust Spatial Prediction via Multi-Resolution Ensembles of Predictive Processes
Nicolas Bianco, Nadja Klein
Gaussian processes provide a flexible framework for spatial prediction, but their computational cost limits applicability to large-scale data with large sample size . Predictive…
From Counting Stations to City-Wide Estimates: Data-Driven Bicycle Volume Extrapolation
Silke K. Kaiser, Nadja Klein, Lynn H. Kaack
Shifting to cycling in urban areas reduces greenhouse gas emissions and improves public health. Street-level bicycle volume information would aid cities in planning targeted infras…