Chris van der Heide
Chris van der Heide
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Stochastic Normalizing Flows
L Hodgkinson, C van der Heide, F Roosta, MW Mahoney
arXiv preprint arXiv:2002.09547, 2020
Shadow Manifold Hamiltonian Monte Carlo
C van der Heide, F Roosta, L Hodgkinson, D Kroese
International Conference on Artificial Intelligence and Statistics, 1477-1485, 2021
Stochastic continuous normalizing flows: training SDEs as ODEs
L Hodgkinson, C van der Heide, F Roosta, MW Mahoney
Uncertainty in Artificial Intelligence, 1130-1140, 2021
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
R Tsuchida, T Pearce, C van der Heide, F Roosta, M Gallagher
arXiv preprint arXiv:2002.08517, 2020
The Interpolating Information Criterion for Overparameterized Models
L Hodgkinson, C van der Heide, R Salomone, F Roosta, MW Mahoney
arXiv preprint arXiv:2307.07785, 2023
Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes
L Hodgkinson, C Van Der Heide, F Roosta, MW Mahoney
International Conference on Machine Learning, 13085-13117, 2023
Partial continuity for nonlinear systems with nonstandard growth and discontinuous coefficients
C van der Heide
arXiv preprint arXiv:1711.01389, 2017
Feasibility detection for nested codesign of hypersonic vehicles
C van der Heide, P Cudmore, I Jahn, V Bone, PM Dower, C Manzie
2023 62nd IEEE Conference on Decision and Control (CDC), 632-637, 2023
Gradient-enhanced deep Gaussian processes for multifidelity modelling
V Bone, C van der Heide, K Mackle, IHJ Jahn, PM Dower, C Manzie
arXiv preprint arXiv:2402.16059, 2024
Developing a Co-Design Framework for Hypersonic Vehicle Aerodynamics and Trajectory
K Mackle, A Lock, I Jahn, C van der Heide
AIAA SCITECH 2024 Forum, 0238, 2024
A PAC-Bayesian Perspective on the Interpolating Information Criterion
L Hodgkinson, C van der Heide, R Salomone, F Roosta, M Mahoney
NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 2023
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