Doucet, Arnaud

108 publications

ICML 2025 Accelerated Diffusion Models via Speculative Sampling Valentin De Bortoli, Alexandre Galashov, Arthur Gretton, Arnaud Doucet

TMLR 2025 Conformalized Credal Regions for Classification with Ambiguous Ground Truth Michele Caprio, David Stutz, Shuo Li, Arnaud Doucet

ICML 2025 Distributional Diffusion Models with Scoring Rules Valentin De Bortoli, Alexandre Galashov, J Swaroop Guntupalli, Guangyao Zhou, Kevin Patrick Murphy, Arthur Gretton, Arnaud Doucet

ICML 2025 Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alan Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov

ICLRW 2025 Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts Marta Skreta, Tara Akhound-Sadegh, Viktor Ohanesian, Roberto Bondesan, Alan Aspuru-Guzik, Arnaud Doucet, Rob Brekelmans, Alexander Tong, Kirill Neklyudov

ALT 2025 Generalisation Under Gradient Descent via Deterministic PAC-Bayes Eugenio Clerico, Tyler Farghly, George Deligiannidis, Benjamin Guedj, Arnaud Doucet

ICLRW 2025 Generalised Parallel Tempering: Flexible Replica Exchange via Flows and Diffusions Leo Zhang, Peter Potaptchik, George Deligiannidis, Arnaud Doucet, Hai-Dang Dau, Saifuddin Syed

AISTATS 2025 Implicit Diffusion: Efficient Optimization Through Stochastic Sampling Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet

NeurIPS 2025 Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities Tara Akhound-Sadegh, Jungyoon Lee, Joey Bose, Valentin De Bortoli, Arnaud Doucet, Michael M. Bronstein, Dominique Beaini, Siamak Ravanbakhsh, Kirill Neklyudov, Alexander Tong

TMLR 2024 Error Bounds for Flow Matching Methods Joe Benton, George Deligiannidis, Arnaud Doucet

ICMLW 2024 Implicit Diffusion: Efficient Optimization Through Stochastic Sampling Pierre Marion, Anna Korba, Peter Bartlett, Mathieu Blondel, Valentin De Bortoli, Arnaud Doucet, Felipe Llinares-López, Courtney Paquette, Quentin Berthet

ICLR 2024 Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization Joe Benton, Valentin De Bortoli, Arnaud Doucet, George Deligiannidis

ICML 2024 Particle Denoising Diffusion Sampler Angus Phillips, Hai-Dang Dau, Michael John Hutchinson, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet

NeurIPS 2024 Schrodinger Bridge Flow for Unpaired Data Translation Valentin De Bortoli, Iryna Korshunova, Andriy Mnih, Arnaud Doucet

NeurIPS 2024 Score-Optimal Diffusion Schedules Christopher Williams, Andrew Campbell, Arnaud Doucet, Saifuddin Syed

NeurIPS 2024 Simplified and Generalized Masked Diffusion for Discrete Data Jiaxin Shi, Kehang Han, Zhe Wang, Arnaud Doucet, Michalis K. Titsias

NeurIPS 2023 A Unified Framework for U-Net Design and Analysis Christopher K. I. Williams, Fabian Falck, George Deligiannidis, Chris C Holmes, Arnaud Doucet, Saifuddin Syed

JMLR 2023 Alpha-Divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics Kamélia Daudel, Joe Benton, Yuyang Shi, Arnaud Doucet

ICMLW 2023 Categorical SDEs with Simplex Diffusion Pierre Harvey Richemond, Sander Dieleman, Arnaud Doucet

TMLR 2023 Conformal Prediction Under Ambiguous Ground Truth David Stutz, Abhijit Guha Roy, Tatiana Matejovicova, Patricia Strachan, Ali Taylan Cemgil, Arnaud Doucet

ICLR 2023 Denoising Diffusion Samplers Francisco Vargas, Will Sussman Grathwohl, Arnaud Doucet

ICMLW 2023 Diffusion Generative Inverse Design Marin Vlastelica, Tatiana Lopez-Guevara, Kelsey R Allen, Peter Battaglia, Arnaud Doucet, Kim Stachenfeld

NeurIPS 2023 Diffusion Schrödinger Bridge Matching Yuyang Shi, Valentin De Bortoli, Andrew Campbell, Arnaud Doucet

NeurIPS 2023 Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits Muhammad Faaiz Taufiq, Arnaud Doucet, Rob Cornish, Jean-Francois Ton

ICML 2023 Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC Yilun Du, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, Will Sussman Grathwohl

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 Trans-Dimensional Generative Modeling via Jump Diffusion Models Andrew Campbell, William Harvey, Christian Weilbach, Valentin De Bortoli, Thomas Rainforth, Arnaud Doucet

NeurIPS 2023 Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus

ALT 2023 Wide Stochastic Networks: Gaussian Limit and PAC-Bayesian Training Eugenio Clerico, George Deligiannidis, Arnaud Doucet

AISTATS 2022 Conditionally Gaussian PAC-Bayes Eugenio Clerico, George Deligiannidis, Arnaud Doucet

AISTATS 2022 Generative Models as Distributions of Functions Emilien Dupont, Yee Whye Teh, Arnaud Doucet

AISTATS 2022 On PAC-Bayesian Reconstruction Guarantees for VAEs Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj

NeurIPS 2022 A Continuous Time Framework for Discrete Denoising Models Andrew Campbell, Joe Benton, Valentin De Bortoli, Thomas Rainforth, George Deligiannidis, Arnaud Doucet

NeurIPS 2022 A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs Fabian Falck, Christopher K. I. Williams, Dominic Danks, George Deligiannidis, Christopher Yau, Chris C Holmes, Arnaud Doucet, Matthew Willetts

TMLR 2022 An Empirical Study of Implicit Regularization in Deep Offline RL Caglar Gulcehre, Srivatsan Srinivasan, Jakub Sygnowski, Georg Ostrovski, Mehrdad Farajtabar, Matthew Hoffman, Razvan Pascanu, Arnaud Doucet

ICLRW 2022 Annealed Importance Sampling Meets Score Matching Arnaud Doucet, Will Sussman Grathwohl, Alexander G. de G. Matthews, Heiko Strathmann

TMLR 2022 COIN++: Neural Compression Across Modalities Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet

COLT 2022 Chained Generalisation Bounds Eugenio Clerico, Amitis Shidani, George Deligiannidis, Arnaud Doucet

UAI 2022 Conditional Simulation Using Diffusion Schrödinger Bridges Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet

NeurIPS 2022 Conformal Off-Policy Prediction in Contextual Bandits Muhammad Faaiz Taufiq, Jean-Francois Ton, Rob Cornish, Yee Whye Teh, Arnaud Doucet

ICML 2022 Continual Repeated Annealed Flow Transport Monte Carlo Alex Matthews, Michael Arbel, Danilo Jimenez Rezende, Arnaud Doucet

JMLR 2022 Efficient MCMC Sampling with Dimension-Free Convergence Rate Using ADMM-Type Splitting Maxime Vono, Daniel Paulin, Arnaud Doucet

ICML 2022 Importance Weighted Kernel Bayes’ Rule Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton

ICLR 2022 Learning Optimal Conformal Classifiers David Stutz, Krishnamurthy Dj Dvijotham, Ali Taylan Cemgil, Arnaud Doucet

UAI 2022 Mitigating Statistical Bias Within Differentially Private Synthetic Data Sahra Ghalebikesabi, Harry Wilde, Jack Jewson, Arnaud Doucet, Sebastian Vollmer, Chris Holmes

JMLR 2022 On Instrumental Variable Regression for Deep Offline Policy Evaluation Yutian Chen, Liyuan Xu, Caglar Gulcehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet

CoRL 2022 Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving Angad Singh, Omar Makhlouf, Maximilian Igl, Joao Messias, Arnaud Doucet, Shimon Whiteson

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

NeurIPS 2022 Score-Based Diffusion Meets Annealed Importance Sampling Arnaud Doucet, Will Grathwohl, Alexander G Matthews, Heiko Strathmann

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

NeurIPS 2022 Towards Learning Universal Hyperparameter Optimizers with Transformers Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Richard Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas

AISTATS 2021 Stable ResNet Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau

ICML 2021 Annealed Flow Transport Monte Carlo Michael Arbel, Alex Matthews, Arnaud Doucet

ICLRW 2021 COIN: COmpression with Implicit Neural Representations Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet

ICML 2021 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet

NeurIPS 2021 Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet

ICML 2021 Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding Yangjun Ruan, Karen Ullrich, Daniel S Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris Maddison

ICLRW 2021 Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish J Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison

ICLR 2021 Learning Deep Features in Instrumental Variable Regression Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton

ICML 2021 Monte Carlo Variational Auto-Encoders Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov

NeurIPS 2021 NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian X Robert

NeurIPS 2021 Online Variational Filtering and Parameter Learning Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet

ICLR 2021 Robust Pruning at Initialization Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh

NeurIPSW 2021 The Curse of Depth in Kernel Regime Soufiane Hayou, Arnaud Doucet, Judith Rousseau

UAI 2021 Unbiased Gradient Estimation for Variational Auto-Encoders Using Coupled Markov Chains Francisco J. R. Ruiz, Michalis K. Titsias, Taylan Cemgil, Arnaud Doucet

UAI 2021 Variational Inference with Continuously-Indexed Normalizing Flows Anthony Caterini, Rob Cornish, Dino Sejdinovic, Arnaud Doucet

NeurIPS 2020 Modular Meta-Learning with Shrinkage Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas

ICML 2020 Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows Rob Cornish, Anthony Caterini, George Deligiannidis, Arnaud Doucet

NeurIPS 2019 Augmented Neural ODEs Emilien Dupont, Arnaud Doucet, Yee Whye Teh

AISTATS 2019 Bernoulli Race Particle Filters Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis

ICML 2019 On the Impact of the Activation Function on Deep Neural Networks Training Soufiane Hayou, Arnaud Doucet, Judith Rousseau

ICML 2019 Replica Conditional Sequential Monte Carlo Alex Shestopaloff, Arnaud Doucet

ICML 2019 Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets Rob Cornish, Paul Vanetti, Alexandre Bouchard-Cote, George Deligiannidis, Arnaud Doucet

AISTATS 2019 Unbiased Smoothing Using Particle Independent Metropolis-Hastings Lawrece Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob

NeurIPS 2018 Hamiltonian Variational Auto-Encoder Anthony L Caterini, Arnaud Doucet, Dino Sejdinovic

NeurIPS 2017 Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet

NeurIPS 2017 Filtering Variational Objectives Chris J Maddison, John Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Teh

JMLR 2017 Generalized P{\'o}lya Urn for Time-Varying Pitman-Yor Processes François Caron, Willie Neiswanger, Frank Wood, Arnaud Doucet, Manuel Davy

JMLR 2017 On Markov Chain Monte Carlo Methods for Tall Data Rémi Bardenet, Arnaud Doucet, Chris Holmes

JMLR 2017 Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models Alexandre Bouchard-Côté, Arnaud Doucet, Andrew Roth

ICLR 2017 Particle Value Functions Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh

ICML 2016 Interacting Particle Markov Chain Monte Carlo Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood

NeurIPS 2015 Expectation Particle Belief Propagation Thibaut Lienart, Yee Whye Teh, Arnaud Doucet

NeurIPS 2014 Asynchronous Anytime Sequential Monte Carlo Brooks Paige, Frank Wood, Arnaud Doucet, Yee Whye Teh

ICML 2014 Fast Computation of Wasserstein Barycenters Marco Cuturi, Arnaud Doucet

ICML 2014 Towards Scaling up Markov Chain Monte Carlo: An Adaptive Subsampling Approach Rémi Bardenet, Arnaud Doucet, Chris Holmes

AISTATS 2009 An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward Matthew Hoffman, Nando Freitas, Arnaud Doucet, Jan Peters

NeurIPS 2009 Bayesian Nonparametric Models on Decomposable Graphs Francois Caron, Arnaud Doucet

UAI 2009 New Inference Strategies for Solving Markov Decision Processes Using Reversible Jump MCMC Matthias Hoffman, Hendrik Kück, Nando de Freitas, Arnaud Doucet

ICML 2008 Sparse Bayesian Nonparametric Regression Francois Caron, Arnaud Doucet

JAIR 2007 A Framework for Kernel-Based Multi-Category Classification Simon I. Hill, Arnaud Doucet

NeurIPS 2007 Bayesian Policy Learning with Trans-Dimensional MCMC Matthew Hoffman, Arnaud Doucet, Nando D. Freitas, Ajay Jasra

UAI 2007 Generalized Polya Urn for Time-Varying Dirichlet Process Mixtures Francois Caron, Manuel Davy, Arnaud Doucet

ICML 2006 Fast Particle Smoothing: If I Had a Million Particles Mike Klaas, Mark Briers, Nando de Freitas, Arnaud Doucet, Simon Maskell, Dustin Lang

ICML 2005 Adapting Two-Class Support Vector Classification Methods to Many Class Problems Simon I. Hill, Arnaud Doucet

UAI 2005 Toward Practical N2 Monte Carlo: The Marginal Particle Filter Mike Klaas, Nando de Freitas, Arnaud Doucet

MLJ 2003 An Introduction to MCMC for Machine Learning Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan

ICCV 2003 Maintaining Multi-Modality Through Mixture Tracking Jaco Vermaak, Arnaud Doucet, Patrick Pérez

NeurIPS 2003 Sequential Bayesian Kernel Regression Jaco Vermaak, Simon J. Godsill, Arnaud Doucet

ICML 2002 Sparse Bayesian Learning for Regression and Classification Using Markov Chain Monte Carlo Shien-Shin Tham, Arnaud Doucet, Kotagiri Ramamohanarao

NeurIPS 2001 Rao-Blackwellised Particle Filtering via Data Augmentation Christophe Andrieu, Nando D. Freitas, Arnaud Doucet

NeCo 2001 Robust Full Bayesian Learning for Radial Basis Networks Christophe Andrieu, Nando de Freitas, Arnaud Doucet

UAI 2000 Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart Russell

UAI 2000 Reversible Jump MCMC Simulated Annealing for Neural Networks Christophe Andrieu, Nando de Freitas, Arnaud Doucet

NeCo 2000 Sequential Monte Carlo Methods to Train Neural Network Models João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee, Arnaud Doucet

NeurIPS 2000 The Unscented Particle Filter Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas, Eric A. Wan

NeurIPS 1999 Robust Full Bayesian Methods for Neural Networks Christophe Andrieu, João F. G. de Freitas, Arnaud Doucet

NeurIPS 1998 Global Optimisation of Neural Network Models via Sequential Sampling João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee