Working papers

• "Permutation-Invariant Spectral Learning via Dyson Diffusion" (with T. Schwarz et al.). arXiv:2510.08535. Pdf
• "On the Asymptotics of Importance Weighted Variational Inference" (with R. Douc, B-E. Cherief-Abdellatif and H. Marival). arXiv:2501.08477. Pdf
• "Prompting Strategies for Enabling Large Language Models to Infer Causation from Correlation" (with E. Sgouritsa et al.). arXiv:2412.13952. Pdf
• "Optimised Annealed Sequential Monte Carlo Samplers" (with S. Syed et al.). arXiv:2408.12057. Pdf
• "Recurrent Gemma: Moving Past Transformers for Efficient Open Language Models" (with A. Botev et al.). arXiv:2404.07839. Pdf
• "Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models" (with S. De et al.). arXiv:2402.19427. Pdf
• "Target Score Matching" (with V De Bortoli, MH Hutchinson & P Wirnsberger). arXiv:2402.08667. Pdf
• "Causal Falsification of Digital Twins" (with R Cornish, MF Taufiq & C Holmes). arXiv:2301.07210. Pdf
• "Continuous diffusion for categorical data" (with S. Dieleman et al.). arXiv:2211.15089. Pdf
• "Spectral Diffusion Processes" (with A. Phillips, T. Seror, M. Hutchinson, V. De Bortoli & E. Mathieu). arXiv:2209.14125. Pdf
• "Metropolis--Hastings with Averaged Acceptance Ratios" (with C. Andrieu, S. Yildirim & N. Chopin). arXiv:2101.01253. Pdf
• "Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design" (with M. Cuturi, O. Teboul, Q. Berthet & J.P. Vert). arXiv:2004.12508. Pdf
• "Ensemble Rejection Sampling" (with G. Deligiannidis & S. Rubenthaler). arXiv:2001.09188. Pdf
• "Schrodinger Bridge Samplers" (with E. Bernton, J. Heng & P.E. Jacob). arXiv:1912.13170. Pdf
• "Mean-field Behaviour of Neural Tangent Kernel for Deep Neural Networks" (with S. Hayou & J. Rousseau). arXiv:1905.13654. Pdf (updated 03/10/19)
• "Hamiltonian Descent Methods" (with C.J. Maddison, D. Paulin, Y.W. Teh & B. O'Donoghue). arXiv:1809.05042. Pdf
• "Scalable Monte Carlo Inference for State-Space Models" (with S. Yildirim & C. Andrieu). arXiv:1809.02527. Pdf
• "Piecewise-Deterministic Markov Chain Monte Carlo" (with P. Vanetti, A. Bouchard-Côté & G. Deligiannidis). arXiv:1707.05296. (updated 15/05/18) Pdf
• "On Embedded Hidden Markov Models and Particle Markov chain Monte Carlo Methods" (with A. Finke & A.M. Johansen). arXiv:1610.08962 Pdf
• "Bayesian Nonparametric Image Segmentation using a Generalized Swedsen-Wang Algorithm" (with R. Xu & F. Caron), arXiv:1602:03048. Pdf
• "Derivative-free Estimation of the Score Vector and Observed Information Matrix with Applications to State-Space Models'' (with P.E. Jacob & S. Rubenthaler), arXiv:1304:5768 (updated 07/2015).
• "Perfect Simulation using Atomic Regeneration with Application to Sequential Monte Carlo" (with A. Lee and K. Łatuszyński), arXiv:1407.5770. Pdf

Edited book

• A. Doucet, N. De Freitas & N.J. Gordon (editors), Sequential Monte Carlo Methods in Practice, Springer-Verlag: New York, 2001.

2026

• "Self-Speculative Masked Diffusions" (with A. Campbell, V. De Bortoli and J. Shi). ICLR 2026. Pdf
• "Learn to Guide Your Diffusion Model" (with A. Galashov et al.). arXiv:2510.00815. ICLR 2026. Pdf
• "Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis" (with T. Farghly, P. Rebeschini and G. Deligiannidis). ICLR 2026. Pdf
• "Accelerated Parallel Tempering via Neural Transports" (with L. Zhang et al.). ICLR 2026. Pdf

2025

• "Simulating Diffusion Bridges using Score Matching" (with V. De Bortoli, J. Thornton & J. Heng). Biometrika, 2025.
• "Conformalized Credal Regions for Classification with Ambiguous Ground Truth" (with M. Carpio, D. Stutz & S. Li). Transactions on Machine Learning Research, 2025.
• "Evaluating AI systems under uncertain ground truth: a case study in dermatology" (with D. Stutz et al.). Medical Image Analysis, 2025.
• "Progressive Inference-Time Annealing of Diffusion Models for Sampling from Boltzmann Densities" (with T. Akhound-Sadegh et al.). NeurIPS, 2025 (Spotlight).
• "Source Separation by Flow Matching" (with R. Scheibler, J.R. Hershey & H. Li). IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), 2025.
• "Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts" (with M. Skreta et al.). ICML, 2025 (Spotlight).
• "Distributional Diffusion Models with Scoring Rules" (with V. De Bortoli et al.). ICML, 2025.
• "Accelerated Diffusion Models via Speculative Sampling" (with V. De Bortoli, A. Galashov and A. Gretton). ICML, 2025.
• "Implicit Diffusion: Efficient Optimization through Stochastic Sampling" (with P. Marion et al.). AISTATS, 2025 (Oral).
• "Generalisation under gradient descent via deterministic PAC-Bayes" (with E. Clerico, B. Guedj and G. Deligiannidis). ALT, 2025.
• "Generalised Parallel Tempering: Flexible Replica Exchange via Flows and Diffusions" (with L. Zhang et al.). ICLR Workshop on Frontiers of Probabilistic Inference, 2025.

2024

• "From Denoising Diffusions to Denoising Markov Models" (with J. Benton, Y. Shi, V. De Bortoli & G. Deligiannidis). Journal of the Royal Statistical Society Series B (with discussion), vol. 86, no. 2, pp. 286-301, 2024.
• "Error Bounds for Flow Matching Methods" (with J. Benton & G. Deligiannidis). Transactions on Machine Learning Research, pp. 1-17, 2024.
• "Diffusion Schrodinger Bridges for Bayesian Computation" (with J. Heng & V. De Bortoli). Statistical Science, vol. 39, no. 1, pp. 90-99, 2024.
• "Qualitative Uniform Stability of the Iterative Proportional Fitting Procedure" (with G. Deligiannidis & V. De Bortoli). Annals of Applied Probability, vol. 34, no. 1A, pp. 501-516, 2024.
• "Solving A Class of Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows" (with F. Crucinio, V. De Bortoli & A.M. Johansen). Stochastic Processes and Their Applications, vol. 173, pp. 1-21, 2024.
• "An Energy-based Model Approach to Rare Event Probability Estimation" (with L. Freidli, D. Ginsbourger & N. Lindle). SIAM Journal on Uncertainty Quantification, 2024.
• "Multivariate stochastic volatility with co-heteroscedasticity" (with J. Chan, R. León-González & R.W. Strachan). Studies in Nonlinear Dynamics & Econometrics, 2024.
• "Nearly d-Linear Convergence Bounds for Diffusion Models via Stochastic Localization" (with J. Benton, V. De Bortoli & G. Deligiannidis). ICLR, 2024 (spotlight). Pdf
• "Mitigating LLM Hallucinations via Conformal Abstention" (with Y. Abbasi-Yadkori et al.). NeurIPS Workshop on Statistical Foundations of LLMs and Foundation Models, 2024.
• "Schrodinger Bridge Flow for Unpaired Data Translation" (with V. De Bortoli, I. Korshunova & A. Mnih). NeurIPS, 2024 (Spotlight).
• "Simplified and Generalized Masked Diffusion for Discrete Data" (with J. Shi, K. Han, Z. Wang & M. Titsias). NeurIPS, 2024.
• "Score-Optimal Diffusion Schedules" (with C.I. Williams, A. Campbell & S. Syed). NeurIPS, 2024.
• "Particle Denoising Diffusion Sampler" (with A. Philipps, H.D. Dau, M.J. Hutchinson, V. De Bortoli & G. Deligiannidis). ICML, 2024.
• "Ranking In Generalized Linear Bandits" (with A. Shidani & G. Deligiannidis). AAAI Workshop on Recommendation Ecosystems: Modeling, Optimization and Incentive Design, 2024 (oral).

2023

• "A Particle Method for Solving Fredholm Equations of the First Kind" (with F. Crucinio & A.M. Johansen). Journal of the American Statistical Association, vol. 118, no. 542, pp. 937--947, 2023. Pdf
• "Alpha-divergence Variational Inference Meets Importance Weighted Autoencoders: Methodology and Asymptotics" (with K. Daudel, J. Benton & Y. Shi). Journal of Machine Learning Research, 2023. Pdf
• "Differentiable Samplers for Deep Latent Variable Models" (with E. Moulines & A. Thin). Philosophical Transactions of the Royal Society A, Theme issue on "Bayesian Inference: challenges, perspective, and prospects'', 2023. Pdf
• "Conformal Prediction under Uncertain Ground Truth" (with D. Stutz, A.G. Roy, T. Matejovicova, P. Strachan & A.T. Cemgil). Transactions on Machine Learning Research, pp. 1--25, 2023. Pdf
• "Trans-Dimensional Generative Modeling via Jump Diffusion Models" (with A. Campbell, W. Harvey, C. Weilbach, V. De Bortoli & T. Rainforth). NeurIPS, 2023 (spotlight). Pdf
• "Diffusion Schrodinger Bridge Matching" (with Y. Shi, V. De Bortoli & A. Campbell). NeurIPS, 2023. Pdf
• "Marginal Density Ratio for Off-Policy Evaluation in Contextual Bandits" (with M. Taufiq, R. Cornish & J.F. Ton). NeurIPS, 2023. Pdf
• "A Unified Framework for U-Net Design and Analysis" (with C. Williams, F. Falck, G. Deligiannidis, C.C. Holmes & S. Syed). NeurIPS, 2023. Pdf
• "Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters" (with M. Noble, V. De Bortoli & A. Durmus). NeurIPS, 2023 (spotlight). Pdf
• "Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC" (with Y. Du, C. Durkan, R. Strudel, J.B, B Tenenbaum, S.Dieleman, R. Fergus, J. Sohl-Dickstein & W. Grathwohl). ICML, 2023. Pdf
• "SE(3) Diffusion Model with Application to Protein Backbone Generation" (with J. Yim, B.L. Trippe, V. De Bortoli, E., R. Barzilay & T. Jaakkola). ICML, 2023. Pdf
• "Denoising Diffusion Samplers" (with F. Vargas & W. Grathwohl). ICLR, 2023. Pdf
• "Wide stochastic networks: Gaussian limit and PAC-Bayesian training" (with E. Clerico & G. Deligiannidis). Algorithmic Learning Theory, 2023. Pdf
• "Categorical SDEs with Simplex Diffusion" (with P.H. Richemond & S. Dieleman). ICML Workshop on Sampling and Optimization in Discrete Space, 2023. Pdf
• "Diffusion Generative Inverse Design" (with M. Vlastelica, T. Lopez-Guevara, K.R. Allen, P. Battaglia & K. Stachenfeld). ICML Workshop on Structured Probabilistic Inference and Generative Modeling, 2023. Pdf

2022

• "Non-reversible Parallel Tempering: A Scalable Highly Parallel MCMC Scheme" (with S. Syed, A. Bouchard-Cote & G. Deligiannidis). Journal of the Royal Statistical Society Series B, vol. 84, no. 2, pp. 321--350, 2022. Pdf
• "Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting" (with M. Vono & D. Paulin). Journal of Machine Learning Research, 2022. Pdf
• "On Instrumental Variable Regression for Deep Offline Policy Evaluation" (with Y Chen, L Xu, C Gulcehre, TL Paine, A Gretton & N de Freitas). Journal of Machine Learning Research, vol. 23, pp. 1--69, 2022. Pdf
• "COIN++: Data Agnostic Data Compression" (with E. Dupont, H. Loya, M. Alizadeh, A. Golinski & Y.W. Teh). Transactions on Machine Learning Research. Pdf
• "Riemmanian Score-Based Generative Modeling" (with V. De Bortoli, E. Mathieu, M. Hutchinson, J. Thornton & Y.W. Teh). NeurIPS 2022 (Outstanding Paper Award - Oral). Pdf
• "A Continuous Time Framework for Discrete Denoising Models" (with A. Campbell, J. Benton, V. de Bortoli, T. Rainforth & G. Deligiannidis). NeurIPS 2022 (Oral). Pdf
• "A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs" (with F. Falck, C. Williams, D. Danks, G. Deligiannidis, C. Yau, C.C. Holmes & M. Willetts). NeurIPS 2022 (Oral).
• "Score-Based Diffusion Meets Annealed Importance Sampling" (with W. Grathwohl, A.G.D.G. Matthews & H. Strathmann). NeurIPS 2022. Pdf
• "Conformal Off-Policy Prediction in Contextual Bandits" (with M.F. Taufiq, J.F. Ton, R. Cornish & Y.W. Teh). NeurIPS 2022. Pdf
• "Towards Learning Universal Hyperparameter Optimizers with Transformers" (with Y. Chen & al.). NeurIPS 2022. Pdf
• "Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving" (with A. Singh, O. Makhlouf, M. Igl, J. Messias & Whiteson). CORL 2022. Pdf
• "Conditional Simulation Using Diffusion Schrödinger Bridges" (with Y. Shi, V. De Bortoli & G. Deligiannidis). UAI 2022. Pdf
• "Mitigating Statistical Bias within Differentially Private Synthetic Data" (with S. Ghalebikesabi, H. Wilde, J. Jewson, S. Vollmer & C.C. Holmes). UAI 2022 (oral). Pdf
• "Chained Generalisation Bounds" (with E. Clerico, A. Shidani & G. Deligiannidis). COLT 2022. Pdf
• "Continual Repeated Annealed Flow Transport Monte Carlo" (with A.G.D.G. Matthews, M. Arbel & D.J. Rezende). ICML 2022. Pdf
• "Importance Weighting Approach in Kernel Bayes' Rule" (with L. Xu, Y. Chen & A. Gretton). ICML 2022. Pdf
• "Learning Optimal Conformal Classifiers" (with D. Stutz, K. Dvijotham & A.T. Cemgil). ICLR 2022 (spotlight). Pdf
• "Generative Models as Distributions of Functions" (with E. Dupont & Y.W. Teh). AISTATS 2022 (oral). Pdf
• "Conditional Gaussian PAC-Bayes" (with E. Clerico & G. Deligiannidis). AISTATS 2022. Pdf
• "On PAC-Bayesian Reconstruction Guarantees for VAEs" (with B-E Cherief-Abdelattif, Y. Shi & B. Guedj). AISTATS 2022. Pdf

2021

• "Gibbs Flow for Approximate Transport with Applications to Bayesian Computation" (with J. Heng & Y. Pokern). Journal of the Royal Statistical Society Series B, vol. 83, no. 1, pp. 156--187, 2021. Pdf
• "Randomized Hamiltonian Monte Carlo as Scaling Limit of the Bouncy Particle Sampler and Dimension-free Convergence Rates" (with G. Deligiannidis, D. Paulin & A. Bouchard-Cote). Annals of Applied Probability, vol. 31, no. 6, pp. 2612--2662, 2021. Pdf
• "Large Sample Asymptotics of the Pseudo-Marginal Method" (with S. Schmon, G. Deligiannidis & M.K. Pitt). Biometrika, vol. 108, no. 1, pp. 37--51, 2021. Pdf
• "Dual Space Preconditioning for Gradient Descent" (with C.J. Maddison, D. Paulin & Y.W. Teh). SIAM Journal on Optimization, vol. 31, no. 1, pp. 991--1016, 2021. Pdf
• "Pseudo-marginal Hamiltonian Monte Carlo" (with J. Alenlov & F. Lindsten). Journal of Machine Learning Research, vol. 22(141), pp. 1--45, 2021. Pdf
• "Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation" (with V.B. Tadic). IEEE Transactions on Information Theory, vol. 67, no. 3, pp. 1825--1848, 2021. Pdf
• "Bias of Particle Approximations to Optimal Filter Derivative" (with V.B. Tadic). SIAM Journal on Control and Optimization, vol. 59, no. 1, pp. 727--748, 2021. Pdf
• "Network Consensus in the Wasserstein Metric Space of Probability Measures" (with A. Bishop). SIAM Journal on Control and Optimization, vol. 59, no. 5, pp. 3251--3277, 2021. Pdf
• "Lithological Tomography with the Correlated Pseudo-Marginal Method" (with L. Freidli, N. Linde & D. Ginsbourger). Geophysical Journal International, to appear. Pdf
• "Online Variational Filtering and Parameter Learning" (with A. Campbell, Y. Shi & T. Rainforth). NeurIPS 2021 (Oral). Pdf
• "Diffusion Schrodinger Bridge with Applications to Score-Based Generative Modeling" (with V. De Bortoli, J. Thornton & J. Heng). NeurIPS 2021 (Spotlight). Pdf
• "NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform" (with A. Thin, Y. Janati El Idrissi, S. Le Corff, C. Ollion, E. Moulines, A. Durmus & C.P. Robert). NeurIPS 2021. Pdf
• "Differentiable Particle Filtering via Entropy-Regularized Optimal Transport" (with A. Corenflos, J. Thornton & G. Deligiannidis). ICML 2021 (long oral). Pdf
• "Annealed Flow Transport Monte Carlo" (with M. Arbel & A.G.D.G. Matthews). ICML 2021 (long oral). Pdf
• "Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding" (with Y. Ruan, K. Ullrich, D. Severo, J. Townsend, A. Khisti, A. Makhzani & C.J. Maddison). ICML 2021 (long oral). Pdf
• "Monte Carlo Variational Auto-Encoders" (with A. Thin, N. Kotelevskii, A. Durmus, M. Panov & E. Moulines). ICML 2021. Pdf
• "Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains" (with F.J. Ruiz, M.K. Titsias & T. Cemgil). UAI 2021. Pdf (Runner-up best paper award)
• "Variational Inference with Continuously-Indexed Normalizing Flows" (with A. Caterini, R. Cornish & D. Sejdinovic). UAI 2021. Pdf
• "Learning Deep Features in Instrumental Variable Regression" (with L. Xu, Y. Chen, S. Srinivasan, N. De Freitas & A. Gretton). ICLR 2021. Pdf
• "Robust Pruning at Initialization" (with S. Hayou, J-F. Ton & Y.W. Teh). ICLR 2021. Pdf
• "COIN: COmpression with Implicit Neural representations" (with E. Dupont, A. Golinski, M. Alizadeh & Y.W. Teh). ICLR Workshop on Neural Compression 2021. Pdf
• "Stable Resnet" (with S. Hayou, E. Clerico, B. He, G. Deligiannidis & J. Rousseau). AISTATS 2021 (oral presentation). Pdf

2019-2020

• "Controlled Sequential Monte Carlo" (with J. Heng, A. Bishop & G. Deligiannidis). Annals of Statistics, vol. 48, no. 5, pp. 2904--2929, 2020. Pdf Matlab code
• "Exponential Ergodicity of the Bouncy Particle Sampler" (with G. Deligiannidis & A. Bouchard-Cote). Annals of Statistics, vol. 47, no. 3, pp. 1268--1287, 2019. Pdf
• "Analyticity of Entropy Rates of Continuous-state Hidden Markov Models" (with V.B. Tadic). IEEE Transactions on Information Theory, vol. 65, no. 12, pp. 7950--7975, 2019. Pdf
• "Limit Theorems for Sequential MCMC Methods" (with A. Finke & A.M. Johansen). Advances in Applied Probability, vol. 52, pp. 377--403, 2020. Pdf
• "Stability of Optimal Filter Higher-Order Derivatives" (with V.B. Tadic). Stochastic Processes and Their Applications, vol. 130, pp. 4808--4858, 2020. Pdf
• "Unbiased MCMC for Intractable Target Distributions" (with L. Middleton, G. Deligiannidis & P.E. Jacob). Electronic Journal of Statistics, vol. 14, no. 2, pp. 2842--2891, 2020. Pdf
• "Non-reversible Jump Algorithms for Bayesian Nested Model Selection" (with P. Gagnon). Journal of Computational and Graphical Statistics, vol. 30, no. 2, pp. 312--323, 2021. Pdf
• "Modular Meta-Learning with Shrinkage" (with Y. Chen et al.). NeurIPS 2020 (spotlight). arXiv:1911.01340. Pdf
• "Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows" (with R. Cornish, A. Caterini & G. Deligiannidis). ICML 2020. Pdf
• "Augmented Neural Ordinary Differential Equations" (with E. Dupont & Y.W. Teh). NeurIPS 2019. Pdf
• "Unbiased Smoothing using Particle Independent Metropolis--Hastings" (with L. Middleton, G. Deligiannidis & P.E. Jacob). AISTATS, 2019. (oral) Pdf Code
• "Bernoulli Race Particle Filters" (with S. Schmon & G. Deligiannidis). AISTATS 2019. Pdf
• "Scalable Metropolis--Hastings for Exact Bayesian Inference with Large Datasets" (with R. Cornish, P. Vanetti, A. Bouchard-Cote & G. Deligiannidis). ICML 2019. (oral) Pdf
• "On the Impact of the Activation Function on Deep Neural Networks Training" (with S. Hayou & J. Rousseau). ICML 2019. Pdf
• "Replica Conditional Sequential Monte Carlo" (with A. Shestopaloff). ICML 2019.
• Discussion of Unbiased MCMC using Couplings by Jacob et al. (with P. Vanetti). J. Roy. Stat. Soc. B, vol. 82, no. 3, pp. 592--593, 2020. Pdf
• Discussion of Quasi-Stationary Monte Carlo and the Scale Algorithm by Pollock et al. (with P. Vanetti). J. Roy. Stat. Soc. B, 2020. Pdf

2017-2018

• "The Correlated Pseudo-Marginal Method" (with G. Deligiannidis & M.K. Pitt). Journal of the Royal Statistical Society Series B, vol. 80, no. 5, pp. 839--870, 2018. Pdf Code
• "The Bouncy Particle Sampler: A Non-Reversible Rejection Free Markov chain Monte Carlo Method" (with A. Bouchard-Cote & S.J. Vollmer). Journal of the American Statistical Association, vol. 113, pp. 855--867, 2018. Pdf Code Piecewise deterministic MCMC library by T. Lienart. See also Msc Thesis by N. Galbraith Pdf with Python code
• "Asymptotic Bias of Stochastic Gradient Search" (with V.B. Tadic). Annals of Applied Probability, vol. 27, no. 6, pp. 3255-3304, 2017 Pdf (extended version Pdf).
• "On Markov chain Monte Carlo Methods for Tall Data'' (with R. Bardenet & C.C. Holmes). Journal of Machine Learning Research, vol. 18(47), pp. 1--43, 2017. Pdf IPython notebook
• "Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models" (with A. Bouchard-Cote & A. Roth). Journal of Machine Learning Research, vol. 18(28), pp. 1--39, 2017. Pdf and code
• "Generalized Polya Urn for Time-Varying Pitman-Yor Processes'' (with F. Caron et al.). Journal of Machine Learning Research, vol. 18(27), pp. 1--32, 2017. Pdf Object tracking code
• "Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains" (with J. Bierkens et al.). Statistics and Probability Letters, vol. 136, pp. 148-154, 2018 Pdf Code.
• "On Uncertainty Quantification in Hydrogeology and Hydrogeophysics" (with N. Linde, D. Ginsbourger, J. Irving & F. Nobile). Advances in Water Resources, vol. 110, pp. 166-181, 2017. Pdf
• "Hamiltonian Variational Auto-Encoder" (with A. Caterini & D. Sejdinovic). NeurIPS 2018 Pdf
• "Filtering Variational Objectives" (with C. Maddison et al.). NIPS 2017. Pdf TensorFlow implementation
• "Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling" (with A. Barbos, F. Caron & J-F. Giovannelli). NIPS 2017. Pdf
• "Particle Value Functions" (with C. Maddison et al.). Proc. Workshop ICLR, 2017. Pdf
• "Sequential Monte Carlo Methods" (with A. Lee). in Handbook of Graphical Models (eds. Maathuis, Drton, Lauritzen & Wainwright), 2018 Pdf.

2015-2016

• "Efficient Implementation of Markov chain Monte Carlo when Using an Unbiased Likelihood Estimator" (with M.K. Pitt, G. Deligiannidis & R. Kohn), Biometrika, vol. 102, no. 2, pp. 295-313, 2015 (+18 pages Supplementary material). Pdf
• "Bayesian Phylogenetic Inference using a Combinatorial Sequential Monte Carlo Method" (with L. Wang & A. Bouchard-Cote). Journal of the American Statistical Association, vol. 110, no. 512, pp. 1362-1374, 2015. Pdf Java code
• "On Particle Methods for Parameter Estimation in State-Space Models" (with N. Kantas et al.). Statistical Science, vol. 30, no. 3, pp. 328-351, 2015. Pdf
• "Uniform Stability of a Particle Approximation of the Optimal Filter Derivative" (with P. Del Moral & S.S. Singh), SIAM J. Control Optimization, vol. 53, no. 3, pp. 1278-1304, 2015. Pdf
• "Particle Methods: An Introduction with Applications" (with P. Del Moral), ESAIM: Proceedings, vol. 44, pp. 1-46, 2014. Pdf
• "Expectation Particle Belief Propagation" (with T. Lienart & Y.W. Teh), NIPS 2015. Code
• "Interacting Particle Markov chain Monte Carlo" (with T. Rainforth et al.), ICML 2016. Code

2013-2014

• "A Lognormal Central Limit Theorem for Particle Approximations of Normalizing Constants" (with J. Berard & P. Del Moral), Electronic J. Proba, vol. 19, no. 94, pp. 1-28, 2014 Pdf.
• "An Online Expectation-Maximization Algorithm for Changepoint Models" (with S. Yildirim & S.S. Singh), J. Comp. Graph. Statist., vol. 22, no. 4, pp. 906-926, 2013. Pdf Matlab code
• "Simulated Likelihood Inference for Stochastic Volatility Models using Continuous Particle Filtering" (with M.K. Pitt & S. Malik), Ann. Inst. Stat. Math., vol 66, pp. 527-552, 2014. Pdf
• "An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration" (with L. Bornn, P. Jacob & P. Del Moral), J. Comp. Graph. Statist., vol. 22, no. 3, pp. 749-773, 2013. Pdf R package PAWL
• "Fast Computation of Wasserstein Barycenters'' (with M. Cuturi), ICML 2014. Pdf
• "Towards Scaling Up MCMC for Large Datasets" (with R. Bardenet & C.C. Holmes), ICML 2014. Pdf Supplementary material
• "Asynchronous Anytime Sequential Monte Carlo" (with B. Paiges, F. Wood & Y.W. Teh), NIPS 2014 (oral).

2011-2012

• "Particle Approximations of the Score and Observed Information Matrix in State-Space Models with Application to Parameter Estimation" (with G. Poyiadjis & S.S. Singh), Biometrika, vol. 98, no. 1, pp. 65-80, 2011. Pdf
• "On the Conditional Distributions of Spatial Point Processes" (with F. Caron, P. Del Moral & M. Pace), Advances in Applied Probability, vol. 43, no. 2, pp. 301-307, 2011. Pdf
• "An Adaptive Sequential Monte Carlo Method for Approximate Bayesian Computation" (with P. Del Moral & A. Jasra), Statist. Computing, vol. 22, no. 5, pp. 1009-1020, 2012. Pdf C code
• "Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors" (with A. Lee, F. Caron & C.C. Holmes), Statistical Applications in Genetics and Molecular Biology, vol. 11, no. 2, 2012. Pdf
• "Efficient Bayesian Inference for Generalized Bradley-Terry Models" (with F. Caron), J. Comp. Graph. Statist., vol. 21, no. 1, pp. 174-196, 2012. Pdf Webpage with Matlab code
• "Particle Approximation of the Intensity Measures of A Spatial Branching Point Process Arising in Multi-target Tracking" (with F. Caron, P. Del Moral & M. Pace), SIAM J. Control Optimization, vol. 49, no. 4, pp. 1766-1792, 2011. Pdf
• "Fluctuations of Interacting Markov Chain Monte Carlo Methods" (with B. Bercu & P. Del Moral), Stochastic Processes and Their Applications, vol. 122, no. 4, pp. 1304-1331, 2012. Pdf
• "Robust Inference on Parameters via Particle Filters and Sandwich Covariance Matrices" (with N. Shephard), Technical report Department of Economics, Oxford University, Discussion paper 606. Pdf
• "Efficient Bayesian Inference for Multivariate Probit Models with Sparse Inverse Correlation Matrices" (with A. Talhouk & K.P. Murphy), J. Comp. Graph. Statist., vol. 21, no. 3, pp. 739-757. Pdf Matlab code
• "On Adaptive Resampling Strategies for Sequential Monte Carlo Methods" (with P. Del Moral & A. Jasra), Bernoulli, vol. 18, no. 1, pp. 252-278, 2012. Pdf
• "On-line Changepoint Detection and Parameter Estimation with Application to Genomic data" (with F. Caron & R. Gottardo), Statist. Computing, vol. 22, no. 2, pp. 579-595, 2012. Pdf
• "Exact approximation of Rao-Blackwellised particle filters" (with A.M. Johansen & N. Whiteley), Proc. 16th IFAC SysId, 2012. Pdf
• "On-line parameter estimation in general state-space models using a pseudo-likelihood approach" (with C. Andrieu & V.B. Tadic), Proc. 16th IFAC SysId, 2012. Pdf
• "Distributed Maximum Likelihood for Simultaneous Self-Localization and Tracking in Sensor Networks" (with N. Kantas & S.S. Singh), IEEE Trans. Signal Processing, 2012. Pdf
• Discussion of Riemannian Manifold Langevin and Hamiltonian Monte Carlo Methods by M. Girolami et al. (with P. Jacob & A.M. Johansen), Journal Royal Statistical Society B, 73(2):162, 2011.
• "A Tutorial on Particle Filtering and Smoothing: Fifteen years Later", (with A.M. Johansen), in Handbook of Nonlinear Filtering (eds. D. Crisan et B. Rozovsky), Oxford University Press, 2011. Pdf
• "On Nonlinear Markov chain Monte Carlo" (with C. Andrieu, A. Jasra & P. Del Moral), Bernoulli, vol. 17, no. 3, pp. 987-1014, 2011. Pdf
• "Efficient Bayesian Inference for Switching State-Space Models using Discrete Particle Markov Chain Monte Carlo methods" (with C. Andrieu & N. Whiteley), Technical report no. 1004 Department of Mathematics Bristol University. Pdf

2009-2010

• "Particle Markov chain Monte Carlo for Efficient Numerical Simulation" (with C. Andrieu & R. Holenstein), in Monte Carlo and Quasi Monte Carlo Methods 2008, Lecture Notes in Statistics, Springer, pp. 45-60, 2009. Pdf
• "Particle Markov chain Monte Carlo methods" (with C. Andrieu & R. Holenstein) (with discussion), Journal Royal Statistical Society B, vol. 72, no. 3, pp. 269-342, 2010. Pdf
• "Forward Smoothing using Sequential Monte Carlo" (with P. Del Moral & S.S. Singh), Technical report Cambridge University TR638, Sept. 2009, revised arXiv:1012:5390. Pdf
• "On the Utility of Graphics Cards to Perform Massively Parallel Implementation of Advanced Monte Carlo Methods" (with A. Lee, C. Yau, M. Giles & C.C. Holmes), J. Comp. Graph. Statist., vol. 19, no. 4, pp. 769-789, 2010. Pdf Website with GPU code
• "A Backward Particle Interpretation of Feynman-Kac Formulae" (with P. Del Moral & S.S. Singh), ESAIM: Math. Model. Num. Analy., Special issue on Probabilistic Methods for PDEs, vol. 44, pp. 947-975, 2010. Pdf
• "On Solving Integral Equations using Markov Chain Monte Carlo Methods" (with A. M. Johansen & V. B. Tadić), Applied Math. Computation, vol. 216, no. 10, pp. 2869-2880, 2010. Pdf
• "A New Class of Interacting Markov chain Monte Carlo Methods" (with P. Del Moral), Comptes rendus Acad. Sci. Math., vol. 348, pp. 79-83, 2010.
• "Interacting Markov chain Monte Carlo Methods for Solving Nonlinear Measured-Valued Equations" (with P. Del Moral), Annals of Applied Probability, vol. 20, no. 2, pp. 593-639, 2010. Pdf
• "Smoothing Algorithms for State-space Models" (with M. Briers & S. Maskell), Annals Institute Statistical Mathematics, vol. 62, no. 1, pp. 61-89, 2010. Pdf
• "A Boosting Approach to Structure Learning of High Dimensional Graphs with and without Prior Knowledge" (with S. Anjum & C.C. Holmes), Bioinformatics, vol. 25, no. 22, pp. 2929-2936, 2009. Pdf
• "An Efficient Computational Approach to Prior Sensitivity and Cross-Validation" (with L. Bornn & R. Gottardo), Canadian Journal of Statistics, vol. 38, no. 1, pp. 47-64, 2010. Pdf
• "Sequentially Interacting Markov chain Monte Carlo" (with A. Brockwell & P. Del Moral), Annals of Statistics, vol. 38, no. 6, pp. 3387-3411, 2010. Pdf
• "A Functional Central Limit Theorem for a Class of Interacting Markov Chain Monte Carlo Methods" (with B. Bercu & P. Del Moral), Electronic Journal of Probability, vol. 73, pp. 2130-2355, 2009. Pdf
• Discussion of Tracking of multiple merging and splitting targets: A statistical perspective by Storlie et al. (with B. Vo), Statistica Sinica.
• "A Bayesian Approach to Joint Tracking and Identification of Geometric Shapes in Video Sequences" (with P. Minvielle, A. Marrs & S. Maskell), Image and Vision Computing, vol. 28, pp. 111-123, 2010. Pdf
• "An Overview of Sequential Monte Carlo Methods for Parameter Estimation in General State-Space Models" (with N. Kantas, S.S. Singh and J.M. Maciejowski), Proceedings IFAC System Identification (SySid) Meeting, 2009.
• "Bayesian Nonparametric Models on Decomposable Graphs" (with F. Caron), NIPS 2009.
• "An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Rewards" (with M. Hoffman, N. De Freitas & J. Peters), AISTATS 2009. Pdf
• "Particle Markov Chain Monte Carlo for Multiple Change-Point Problems" (with C. Andrieu & N. Whiteley), Technical report no. 0911 Department of Mathematics Bristol University, 2009. Pdf
• "A Hierarchical Bayesian Framework for Constructing Sparsity-inducing Priors" (with A. Lee, F. Caron & C.C. Holmes), 2009. Pdf
• A Note on Efficient Conditional Simulation of Gaussian Distributions Pdf

2007-2008

• "Generalized Polya urn for time-varying Dirichlet processes", (with F. Caron & M. Davy), UAI 2007 Pdf
• "Sparse Bayesian Nonparametric Regression", (with F. Caron), ICML 2008. Pdf
• "Simulation-Based Optimal Sensor Scheduling with Application to Observer Trajectory Planning", (with S.S. Singh et al.), Automatica, vol. 43, no. 5, pp. 817-830, 2007. Pdf
• "A Note on Auxiliary Particle Filters", (with A.M. Johansen), Statistics and Probability Letters, vol. 78, pp. 1498-1504, 2008. Pdf
• "Particle methods for Maximum Likelihood Parameter Estimation in Latent Variable Models", (with A.M. Johansen & M. Davy), Statistics and Computing, vol. 18, pp. 47-57, 2008. Pdf
• "A Note on the Convergence of the Equi-Energy Sampler", (with C. Andrieu, A. Jasra & P. Del Moral), Stochastic Analysis and Applications, vol. 26, pp. 298-312, 2008. Pdf
• "Sharp Propagation of Chaos Estimates for Feynman-Kac Particle Models", (with P. Del Moral & G.W. Peters), Teoriya Veroyatnostei i ee Primeneniya, vol. 51, no. 3, 2006. Reprinted in SIAM Theory of Probability and Its Applications, vol. 51, no. 3, pp. 459-485, 2007 Pdf
• "A Framework for Kernel-Based Multi-Category Classification", (with S.I. Hill), J. Artificial Intell. Res., vol. 30, pp. 525-564, 2007. Pdf
• "Bayesian policy learning with trans-dimensional MCMC", (with M. Hoffman, N. De Freitas & A. Jasra), NIPS 2007. Pdf

2005-2006

• "Sequential Monte Carlo Samplers", (with P. Del Moral & A. Jasra), J. Royal Statist. Soc. B, vol. 68, no. 3, pp. 411-436, 2006. Pdf Additional note Website with C++ code Google Scholar Classic paper
• "Efficient Block Sampling Strategies for Sequential Monte Carlo", (with M. Briers & S. Senecal), J. Comp. Graph. Statist., vol. 15, no. 3, pp. 693-711, 2006. Pdf
• "Sequential Monte Carlo methods for Bayesian Multi-target filtering with Random Finite Sets", (with B. Vo & S.S. Singh), IEEE Trans. Aerospace Elec. Systems, vol. 41, pp. 1224-1245, 2005. Pdf
• "Sequential Monte Carlo for Bayesian Computation" with discussion, (with P. Del Moral & A. Jasra), Bayesian Statistics 8, Oxford University Press, 2006. Pdf preliminary version here
• "Convergence of the SMC Implementation of the PHD Filter", (with A. Johansen, S.S. Singh & B. Vo), Methodology and Computing in Applied Probability, vol. 8, no. 2, pp. 265-291, 2006. Pdf
• Discussion of Exact and Computationally Efficient Likelihood Estimation of Discretely Observed Diffusions by Beskos et al. (with M. Rousset) J. Royal Statist. Soc. B, 2006. Pdf
• "Exponential Forgetting and Geometric Ergodicity in General State-Space Models", (with V.B. Tadic), Stochastic Processes and Their Applications, vol. 115, pp. 1408-1436, 2005. Pdf
• "Fast Particle Smoothing: If I Had a Million Particles", (with M. Klass et al.), ICML 2006 Pdf
• "Space Alternating Data Augmentation: Application to Finite Mixture of Gaussians and Speaker Recognition" (with T. Matsui & S. Senecal), Proc. IEEE ICASSP, 2005. Pdf
• "Towards practical N^2 Monte Carlo: The marginal particle filter", (with M. Klaas & N. De Freitas), UAI 2005 Pdf
• "Online simulation-based methods for parameter estimation in non linear non Gaussian state-space models", (with C. Andrieu & V.B. Tadic), Proc. IEEE CDC (invited paper), 2005 Pdf
• "Particle methods for optimal filter derivative: Application to parameter estimation", (with G. Poyadjis & S.S. Singh), Proc. IEEE ICASSP (invited paper), 2005 Pdf Extended version of this paper Maximum Likelihood Parameter Estimation using Particle Methods, Joint Statistical Meeting, Pdf
• "Sequential Monte Carlo samplers for rare events", (with P. Del Moral & A.M. Johansen), Proc. 6th International Workshop on Rare Event Simulation, 2006 Pdf
• "Sequential sampling for dynamic environment map illumination", (with A. Ghosh & W. Heidrich), Proc. Eurographics Symposium on Rendering, 2006 Pdf

2003-2004

• "Particle Motions in Absorbing Medium with Hard and Soft Obstacles", (with P. Del Moral), Stochastic Analysis and Applications, vol. 22, no. 5, pp. 1175-1207, 2004. Pdf
• "Monte Carlo Smoothing for Nonlinear Time Series", (with S.J. Godsill & M. West), J. Amer. Stat. Assoc., vol. 99, no. 465, pp. 156-168, 2004.
• "Computational Methods for and from Bayesian Analysis", (with C. Andrieu & C.P. Robert), Statistical Science, vol. 19, no. 1, 2004. Pdf
• "Reversible Jump MCMC Strategies for Bayesian model selection in Autoregressive Processes", (with J. Vermaak, C. Andrieu & S.J. Godsill), J. Time Series Analysis, vol. 25, no. 6, pp. 785-809, 2004. Pdf
• Discussion on Efficient Construction of Reversible Jump MCMC Proposal Distributions by Brooks et al. (with C. Andrieu) J. Royal Statist. Soc. B, 2003.
• "On a Class of Genealogical and Interacting Metropolis Models" (with P. Del Moral). Seminaire de Proba. XXXVII, Ed. J. Azema, M. Emery, M. Ledoux & M. Yor, Lecture Notes in Mathematics, Springer-Verlag Berlin, 2003. Pdf
• "Efficient Particle Filtering for Jump Markov Systems - Applications to Time-Varying Autoregressions", (with C. Andrieu & M. Davy), IEEE Trans. Signal Processing, vol. 51, no. 7, pp. 1762-1770, 2003 Pdf
• "An Introduction to MCMC for Machine Learning", (with C. Andrieu, N. de Freitas & M.I. Jordan), Machine Learning, vol. 50, pp. 5-43, 2003. Pdf
• "Parameter Estimation in General State-Space Models using Particle Methods" (with V.B. Tadic), Ann. Inst. Stat. Math., vol. 55, no. 2, pp. 409-422, 2003. Read Biometrika 2010 Pdf instead.
• "Maintining Multimodality through Mixture Tracking", (with J. Vermaak & P. Perez), ICCV 2003 Download it here

2001-2002

• "Particle Filtering for Partially Observed Gaussian State Space Models", (with C. Andrieu), J. Royal Statist. Soc. B, vol. 64, no.4, pp. 827-836, 2002. Pdf
• "Particle Filters for State Estimation of Jump Markov Linear Systems" (with N.J. Gordon and V. Krishnamurthy), IEEE Trans. Signal Processing, vol. 49, no.3, pp. 613-624, 2001. Pdf
• "Convergence of Simulated Annealing using Foster-Lyapunov Criteria", (with C. Andrieu & L. Breyer), J. Applied Probability, vol. 38, no. 4, pp. 975-994, 2001. Pdf
• "Robust Full Bayesian Learning for Radial Basis Networks" (with C. Andrieu and J.F.G. de Freitas), Neural Computation, vol. 13, pp. 2359-2407, 2001. Pdf
• "Marginal Maximum A Posteriori Estimation using MCMC" (with S.J. Godsill & C.P. Robert), Statistics and Computing, vol. 12, pp. 77-84, 2002. Pdf
• "A Survey of Convergence Results on Particle Filtering for Practitioners", (with D. Crisan), IEEE Trans. Signal Processing, vol. 50, no. 3, pp. 736-746, 2002. Pdf
• "Bayesian Curve Fitting with Applications to Signal Segmentation", (with E. Punskaya, C. Andrieu & W.J. Fitzgerald), IEEE Trans. Signal Processing, vol. 50, no. 3, pp. 747-758, 2002. Pdf
• "Maximum a Posteriori Sequence Estimation via Monte Carlo Particle Methods" (with S.J. Godsill & M. West), Ann. Inst. Stat. Math. vol. 53, no. 1, pp. 82-96, 2001.
• "Optimal Estimation and Cramer-Rao Bounds for Partial Non-Gaussian State-Space Models" (with N. Bergman & N.J. Gordon), Ann. Inst. Stat. Math., vol. 52, no. 1, pp. 97-112, 2001. Pdf
• "Iterative Algorithms for State Estimation of Jump Markov Linear Systems" (with C. Andrieu), IEEE Trans. Signal Processing, vol. 49, no. 6, pp. 1216-1227, 2001. Pdf
• "Bayesian Deconvolution of Noisy Filtered Point Processes" (with C. Andrieu & E. Barat), IEEE Trans. Signal Processing, vol. 49, no. 1, pp. 134-146, 2001. Pdf
• "Rao-Blackwellised Particle Filtering via Data Augmentation", (with C. Andrieu & N. de Freitas), NIPS 2001. Pdf
• "An Introduction to Sequential Monte Carlo Methods" (with J.F.G. de Freitas & N.J. Gordon) in Sequential Monte Carlo Methods in Practice, New York: Springer-Verlag, January 2001. Pdf
• "Sequential Monte Carlo Methods for Optimal Filtering" (with C. Andrieu & E. Punskaya) in Sequential Monte Carlo Methods in Practice, New York: Springer-Verlag, January 2001.
• "Sparse Bayesian Learning for Regression and Classification using MCMC", (with S.S. Tham & R. Kitagari), ICML 2002.
• "Particle methods for Bayesian modeling and enhancement of speech signals", (with J. Vermaak, C. Andrieu & S.J. Godsill), IEEE Trans. Speech and Audio Proc., vol. 10, no. 3, pp. 173-185, 2002. Pdf

1995-2000

• "On Sequential Monte Carlo Sampling Methods for Bayesian Filtering" (with S.J. Godsill & C. Andrieu), Statistics and Computing, vol. 10, no. 3, pp. 197-208, 2000 Pdf (journal paper version of technical report Cambridge University CUED/F-INFENG/TR310 "On sequential simulation-based methods for Bayesian filtering", 1998 Pdf) This paper is a fast breaking paper and has been reprinted in A. Harvey & T. Proietti (eds), Readings in Unobserved Components Models, Series Advanced Texts in Econometrics, Oxford University Press, 2005.
• "Convergence of Sequential Monte Carlo Methods'' (with D. Crisan), Technical report, Cambridge University CUED/F-INFENG/TR381, 2000 (never appeared) Pdf
• "Stochastic Sampling Algorithms for State Estimation of Jump Markov Linear Systems'' (with A. Logothetis & V. Krishnamurthy), IEEE Trans. Automatic Control, vol. 45, no. 2, pp. 188-201, 2000. Pdf
• "Simulated Annealing for Maximum A Posteriori Parameter Estimation of Hidden Markov Models" (with C. Andrieu), IEEE Trans. Information Theory, vol. 46, no. 3, pp. 994-1004, 2000.
• "Reversible jump simulated annealing for RBFs", (with C. Andrieu & N. de Freitas), UAI 2000. Pdf
• "Rao-Blackwellised particle filtering for dynamic Bayesian networks", (with N. de Freitas, K. Murphy & S. Russell), UAI 2000. Pdf
• "The Unscented Particle Filter", (with R van der Merwe, N. de Freitas & E Wan), NIPS 2000 Pdf Longer Report
• "MCMC data association for target tracking", (with N. Bergman), Proc. IEEE ICASSP, 2000. Download it here
• "Sequential MCMC for Bayesian Model Selection", (with C. Andrieu & N. de Freitas, ) Proc. IEEE HOS, 1999 (invited paper). Pdf
• "Simulation-Based Methods for Blind Maximum-Likelihood Linear System Identification", (with O. Cappe, E. Moulines & M. Lavielle), Signal Processing, vol. 73, no. 1-2, pp. 3-25, 1999.
• "An Improved Method for Simulation of Real Stable ARMA(p,q) processes" (with C. Andrieu), IEEE Signal Proc. Letters, vol. 6, no.6, pp. 142-144, 1999.
• "Joint Bayesian Detection and Estimation of Noisy Sinusoids via Reversible Jump MCMC" (with C. Andrieu), IEEE Trans. Signal Processing, vol. 47, no. 10, pp. 2667-2676, 1999 Pdf

Rejected paper

• "Filtrage Optimal et Sous-Optimal des Signaux de Rayonnements" (never appeared): my first journal paper (written in Word) submitted to Traitement du Signal in 1995 and rejected. I was about to give up academia after this promising beginning but eventually didn't and tried harder.