Mathieu, Emile

21 publications

NeurIPS 2024 DEFT: Efficient Fine-Tuning of Diffusion Models by Learning the Generalised $h$-Transform Alexander Denker, Francisco Vargas, Shreyas Padhy, Kieran Didi, Simon Mathis, Vincent Dutordoir, Riccardo Barbano, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

ICLRW 2024 Guided Autoregressive Diffusion Models with Applications to PDE Simulation Federico Bergamin, Cristiana Diaconu, Aliaksandra Shysheya, Paris Perdikaris, José Miguel Hernández-Lobato, Richard E. Turner, Emile Mathieu

TMLR 2024 Improved Motif-Scaffolding with SE(3) Flow Matching Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, Jose Jimenez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noe, Regina Barzilay, Tommi Jaakkola

ICLRW 2024 Improved Motif-Scaffolding with SE(3) Flow Matching Jason Yim, Andrew Campbell, Emile Mathieu, Andrew Y. K. Foong, Michael Gastegger, Jose Jimenez-Luna, Sarah Lewis, Victor Garcia Satorras, Bastiaan S. Veeling, Frank Noe, Regina Barzilay, Tommi Jaakkola

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

NeurIPSW 2023 A Framework for Conditional Diffusion Modelling with Applications in Motif Scaffolding for Protein Design Kieran Didi, Francisco Vargas, Simon Mathis, Vincent Dutordoir, Emile Mathieu, Urszula Julia Komorowska, Pietro Lio

TMLR 2023 Diffusion Models for Constrained Domains Nic Fishman, Leo Klarner, Valentin De Bortoli, Emile Mathieu, Michael John Hutchinson

NeurIPS 2023 Geometric Neural Diffusion Processes Emile Mathieu, Vincent Dutordoir, Michael Hutchinson, Valentin De Bortoli, Yee Whye Teh, Richard Turner

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

NeurIPS 2023 Metropolis Sampling for Constrained Diffusion Models Nic Fishman, Leo Klarner, Emile Mathieu, Michael Hutchinson, Valentin De Bortoli

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 Jaakkola

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

NeurIPSW 2023 SE(3) Equivariant Augmented Coupling Flows Laurence Illing Midgley, Vincent Stimper, Javier Antoran, Emile Mathieu, Bernhard Schölkopf, José Miguel Hernández-Lobato

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

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

NeurIPSW 2022 Spectral Diffusion Processes Angus Phillips, Thomas Seror, Michael John Hutchinson, Valentin De Bortoli, Arnaud Doucet, Emile Mathieu

NeurIPS 2021 On Contrastive Representations of Stochastic Processes Emile Mathieu, Adam Foster, Yee W. Teh

NeurIPS 2020 Riemannian Continuous Normalizing Flows Emile Mathieu, Maximilian Nickel

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

ICML 2019 Disentangling Disentanglement in Variational Autoencoders Emile Mathieu, Tom Rainforth, N Siddharth, Yee Whye Teh

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