Arnaud Doucet

Accelerated Diffusion Models via Speculative Sampling ICML 2025 Distributional Diffusion Models with Scoring Rules ICML 2025 Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts ICML 2025 Generalisation under gradient descent via deterministic PAC-Bayes ALT 2025 Implicit Diffusion: Efficient optimization through stochastic sampling AISTATS 2025 Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization ICLR 2024 Particle Denoising Diffusion Sampler ICML 2024 Schrodinger Bridge Flow for Unpaired Data Translation NeurIPS 2024 Score-Optimal Diffusion Schedules NeurIPS 2024 Simplified and Generalized Masked Diffusion for Discrete Data NeurIPS 2024 Alpha-divergence Variational Inference Meets Importance Weighted Auto-Encoders: Methodology and Asymptotics JMLR 2023 A Unified Framework for U-Net Design and Analysis NeurIPS 2023 Denoising Diffusion Samplers ICLR 2023 Diffusion Schrödinger Bridge Matching NeurIPS 2023 Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits NeurIPS 2023 Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC ICML 2023 SE(3) diffusion model with application to protein backbone generation ICML 2023 Trans-Dimensional Generative Modeling via Jump Diffusion Models NeurIPS 2023 Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters NeurIPS 2023 Wide stochastic networks: Gaussian limit and PAC-Bayesian training ALT 2023 A Continuous Time Framework for Discrete Denoising Models NeurIPS 2022 A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs NeurIPS 2022 Chained generalisation bounds COLT 2022 Conditionally Gaussian PAC-Bayes AISTATS 2022 Conditional simulation using diffusion Schrödinger bridges UAI 2022 Conformal Off-Policy Prediction in Contextual Bandits NeurIPS 2022 Continual Repeated Annealed Flow Transport Monte Carlo ICML 2022 Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting JMLR 2022 Generative Models as Distributions of Functions AISTATS 2022 Importance Weighted Kernel Bayes’ Rule ICML 2022 Learning Optimal Conformal Classifiers ICLR 2022 Mitigating statistical bias within differentially private synthetic data UAI 2022 On Instrumental Variable Regression for Deep Offline Policy Evaluation JMLR 2022 On PAC-Bayesian reconstruction guarantees for VAEs AISTATS 2022 Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving CoRL 2022 Riemannian Score-Based Generative Modelling NeurIPS 2022 Score-Based Diffusion meets Annealed Importance Sampling NeurIPS 2022 Towards Learning Universal Hyperparameter Optimizers with Transformers NeurIPS 2022 Annealed Flow Transport Monte Carlo ICML 2021 Differentiable Particle Filtering via Entropy-Regularized Optimal Transport ICML 2021 Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling NeurIPS 2021 Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding ICML 2021 Learning Deep Features in Instrumental Variable Regression ICLR 2021 Monte Carlo Variational Auto-Encoders ICML 2021 NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform NeurIPS 2021 Online Variational Filtering and Parameter Learning NeurIPS 2021 Robust Pruning at Initialization ICLR 2021 Stable ResNet AISTATS 2021 Unbiased gradient estimation for variational auto-encoders using coupled Markov chains UAI 2021 Variational inference with continuously-indexed normalizing flows UAI 2021 Modular Meta-Learning with Shrinkage NeurIPS 2020 Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows ICML 2020 Augmented Neural ODEs NeurIPS 2019 Bernoulli Race Particle Filters AISTATS 2019 On the Impact of the Activation function on Deep Neural Networks Training ICML 2019 Replica Conditional Sequential Monte Carlo ICML 2019 Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets ICML 2019 Unbiased Smoothing using Particle Independent Metropolis-Hastings AISTATS 2019 Hamiltonian Variational Auto-Encoder NeurIPS 2018 Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling NeurIPS 2017 Filtering Variational Objectives NeurIPS 2017 Generalized Pólya Urn for Time-Varying Pitman-Yor Processes JMLR 2017 On Markov chain Monte Carlo methods for tall data JMLR 2017 Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models JMLR 2017 Interacting Particle Markov Chain Monte Carlo ICML 2016 Expectation Particle Belief Propagation NeurIPS 2015 Asynchronous Anytime Sequential Monte Carlo NeurIPS 2014 Fast Computation of Wasserstein Barycenters ICML 2014 Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach ICML 2014 Bayesian Nonparametric Models on Decomposable Graphs NeurIPS 2009 Active Policy Learning for Robot Planning and Exploration under Uncertainty RSS 2007 Bayesian Policy Learning with Trans-Dimensional MCMC NeurIPS 2007 An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward AISTATS 2005