Michael J. Hutchinson

According to our database, Michael J. Hutchinson authored at least 16 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs.

Peter Mostowsky, Vincent Dutordoir, Iskander Azangulov, Noémie Jaquier, Michael John Hutchinson, Aditya Ravuri, Leonel Rozo, Alexander Terenin, Viacheslav Borovitskiy

CoRR, 2024

Target Score Matching.

Valentin De Bortoli, Michael J. Hutchinson, Peter Wirnsberger, Arnaud Doucet

CoRR, 2024

Particle Denoising Diffusion Sampler.

Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Diffusion Models for Constrained Domains.

Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson

Trans. Mach. Learn. Res., 2023

Geometric Neural Diffusion Processes.

Emile Mathieu, Vincent Dutordoir, Michael J. Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard E. Turner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Metropolis Sampling for Constrained Diffusion Models.

Nic Fishman, Leo Klarner, Emile Mathieu, Michael J. Hutchinson, Valentin De Bortoli

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Spectral Diffusion Processes.

Angus Phillips, Thomas Seror, Michael J. Hutchinson, Valentin De Bortoli, Arnaud Doucet, Emile Mathieu

CoRR, 2022

Riemannian Diffusion Schrödinger Bridge.

James Thornton, Michael J. Hutchinson, Emile Mathieu, Valentin De Bortoli, Yee Whye Teh, Arnaud Doucet

CoRR, 2022

Riemannian Score-Based Generative Modeling.

Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet

CoRR, 2022

Riemannian Score-Based Generative Modelling.

Valentin De Bortoli, Emile Mathieu, Michael J. Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Equivariant Projected Kernels.

Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth

CoRR, 2021

Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels.

Michael J. Hutchinson, Alexander Terenin, Viacheslav Borovitskiy, So Takao, Yee Whye Teh, Marc Peter Deisenroth

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

LieTransformer: Equivariant Self-Attention for Lie Groups.

Michael J. Hutchinson, Charline Le Lan, Sheheryar Zaidi, Emilien Dupont, Yee Whye Teh, Hyunjik Kim

Proceedings of the 38th International Conference on Machine Learning, 2021

Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural Processes.

Peter Holderrieth, Michael J. Hutchinson, Yee Whye Teh

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Equivariant Conditional Neural Processes.

Peter Holderrieth, Michael J. Hutchinson, Yee Whye Teh

CoRR, 2020

2019

Differentially Private Federated Variational Inference.

Mrinank Sharma, Michael J. Hutchinson, Siddharth Swaroop, Antti Honkela, Richard E. Turner

CoRR, 2019