Sampling and Inference for Beta Neutral-to-the-Left Models of Sparse Networks. - Benjamin Bloem-Reddy; Adam Foster 001; Emile Mathieu; Yee Whye Teh (UAI, 2018)

Disentangling Disentanglement. - Emile Mathieu; Tom Rainforth; Siddharth Narayanaswamy; Yee Whye Teh (CoRR, 2018)

Disentangling Disentanglement in Variational Autoencoders. - Emile Mathieu; Tom Rainforth; N. Siddharth 001; Yee Whye Teh (ICML, 2019)

Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders. - Emile Mathieu; Charline Le Lan; Chris J. Maddison; Ryota Tomioka; Yee Whye Teh (NeurIPS, 2019)

Hierarchical Representations with Poincaré Variational Auto-Encoders. - Emile Mathieu; Charline Le Lan; Chris J. Maddison; Ryota Tomioka; Yee Whye Teh (CoRR, 2019)

Riemannian Continuous Normalizing Flows. - Emile Mathieu; Maximilian Nickel (NeurIPS, 2020)

On Contrastive Representations of Stochastic Processes. - Emile Mathieu; Adam Foster 001; Yee Whye Teh (NeurIPS, 2021)

InteL-VAEs: Adding Inductive Biases to Variational Auto-Encoders via Intermediary Latents. - Ning Miao; Emile Mathieu; N. Siddharth 001; Yee Whye Teh; Tom Rainforth (CoRR, 2021)

On Incorporating Inductive Biases into VAEs. - Ning Miao; Emile Mathieu; Siddharth N; Yee Whye Teh; Tom Rainforth (ICLR, 2022)

Riemannian Score-Based Generative Modelling. - Valentin De Bortoli; Emile Mathieu; Michael J. Hutchinson; James Thornton; Yee Whye Teh; Arnaud Doucet (NeurIPS, 2022)

Riemannian Score-Based Generative Modeling. - Valentin De Bortoli; Emile Mathieu; Michael J. Hutchinson; James Thornton; Yee Whye Teh; Arnaud Doucet (CoRR, 2022)

Learning Instance-Specific Data Augmentations. - Ning Miao; Emile Mathieu; Yann Dubois; Tom Rainforth; Yee Whye Teh; Adam Foster 001; Hyunjik Kim (CoRR, 2022)

Riemannian Diffusion Schrödinger Bridge. - James Thornton; Michael J. Hutchinson; Emile Mathieu; Valentin De Bortoli; Yee Whye Teh; Arnaud Doucet (CoRR, 2022)

Spectral Diffusion Processes. - Angus Phillips; Thomas Seror; Michael J. Hutchinson; Valentin De Bortoli; Arnaud Doucet; Emile Mathieu (CoRR, 2022)

Learning Instance-Specific Augmentations by Capturing Local Invariances. - Ning Miao; Tom Rainforth; Emile Mathieu; Yann Dubois; Yee Whye Teh; Adam Foster 001; Hyunjik Kim (ICML, 2023)

SE(3) diffusion model with application to protein backbone generation. - Jason Yim; Brian L. Trippe; Valentin De Bortoli; Emile Mathieu; Arnaud Doucet; Regina Barzilay; Tommi S. Jaakkola (ICML, 2023)

Metropolis Sampling for Constrained Diffusion Models. - Nic Fishman; Leo Klarner; Emile Mathieu; Michael J. Hutchinson; Valentin De Bortoli (NeurIPS, 2023)

Geometric Neural Diffusion Processes. - Emile Mathieu; Vincent Dutordoir; Michael J. Hutchinson; Valentin De Bortoli; Yee Whye Teh; Richard E. Turner (NeurIPS, 2023)

SE(3) Equivariant Augmented Coupling Flows. - Laurence I. Midgley; Vincent Stimper; Javier Antorán; Emile Mathieu; Bernhard Schölkopf; José Miguel Hernández-Lobato (NeurIPS, 2023)

A framework for conditional diffusion modelling with applications in motif scaffolding for protein design. - Kieran Didi; Francisco Vargas 001; Simon V. Mathis; Vincent Dutordoir; Emile Mathieu; Urszula Julia Komorowska; Pietro Lio (CoRR, 2023)

Diffusion Models for Constrained Domains. - Nic Fishman; Leo Klarner; Valentin De Bortoli; Emile Mathieu; Michael John Hutchinson (Trans. Mach. Learn. Res., 2023)

DEFT: Efficient Fine-tuning of Diffusion Models by Learning the Generalised $h$-transform. - Alexander Denker; Francisco Vargas 001; Shreyas Padhy; Kieran Didi; Simon V. Mathis; Riccardo Barbano; Vincent Dutordoir; Emile Mathieu; Urszula Julia Komorowska; Pietro Lió (NeurIPS, 2024)

On conditional diffusion models for PDE simulations. - Aliaksandra Shysheya; Cristiana Diaconu; Federico Bergamin; Paris Perdikaris; José Miguel Hernández-Lobato; Richard E. Turner; Emile Mathieu (NeurIPS, 2024)

DEFT: Efficient Finetuning of Conditional Diffusion Models by Learning the Generalised h-transform. - Alexander Denker; Francisco Vargas 001; Shreyas Padhy; Kieran Didi; Simon V. Mathis; Vincent Dutordoir; Riccardo Barbano; Emile Mathieu; Urszula Julia Komorowska; Pietro Lio (CoRR, 2024)

Improved motif-scaffolding with SE(3) flow matching. - Jason Yim; Andrew Campbell; Emile Mathieu; Andrew Y. K. Foong; Michael Gastegger; José Jiménez-Luna; Sarah Lewis; Victor Garcia Satorras; Bastiaan S. Veeling; Frank Noé; Regina Barzilay; Tommi S. Jaakkola (Trans. Mach. Learn. Res., 2024)