A. Doucet

Christophe Andrieu, Arnaud Doucet, A. Doucet, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

Stochastic algorithms for marginal MAP retrieval of sinusoids in non-Gaussian noise

A. Doucet, C. Andrieu, 2000, Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496).

Online sampling for parameter estimation in general state space models

A. Doucet, V. Tadić, C. Andrieu, 2003 .

Nonreversible Jump Algorithms for Bayesian Nested Model Selection

Arnaud Doucet, Philippe Gagnon, A. Doucet, 2019, 1911.01340.

Piecewise-Deterministic Markov Chain Monte Carlo

A. Doucet, George Deligiannidis, Alexandre Bouchard-Cot'e, 2017, 1707.05296.

Reversible Jump Markov Chain Monte Carlo Strategies for Bayesian Model Selection in Autoregressive Processes

A. Doucet, C. Andrieu, S. Godsill, 2004, Journal of Time Series Analysis.

Non‐reversible parallel tempering: A scalable highly parallel MCMC scheme

A. Doucet, George Deligiannidis, A. Bouchard-Côté, 2019, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

On-line Bayesian modelling and enhancement of speech signals

Simon J. Godsill, Christophe Andrieu, Arnaud Doucet, 2000 .

Uniform Stability of a Particle Approximation of the Optimal Filter Derivative

Arnaud Doucet, Pierre Del Moral, Sumeetpal S. Singh, 2011, SIAM J. Control. Optim..

Particle Markov Chain Monte Carlo for Efficient Numerical Simulation

Christophe Andrieu, Arnaud Doucet, Roman Holenstein, 2009 .

State estimation of jump Markov linear systems via stochastic sampling algorithms

A. Doucet, V. Krishnamurthy, A. Logothetis, 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

Markov chain Monte Carlo data association for target tracking

Arnaud Doucet, Niclas Bergman, A. Doucet, 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

Optimisation of particle filters using simultaneous perturbation stochastic approximation

Arnaud Doucet, Vladislav B. Tadic, Bao Ling Chan, 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

On-line optimization of sequential Monte Carlo methods using stochastic approximation

A. Doucet, V. Tadić, V. Tadic, 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

Bayesian curve fitting using MCMC with applications to signal segmentation

Christophe Andrieu, Arnaud Doucet, William J. Fitzgerald, 2002, IEEE Trans. Signal Process..

Bayesian Blind and Semi-Blind Equalization of Channels with Markov Inputs

Christophe Andrieu, Arnaud Doucet, R. Urien, 2001 .

Bayesian blind marginal separation of convolutively mixed discrete sources

A. Doucet, C. Andrieu, S. Godsill, 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

Maintaining multimodality through mixture tracking

Patrick Pérez, Arnaud Doucet, Jaco Vermaak, 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

Maximum a Posteriori Sequence Estimation Using Monte Carlo Particle Filters

A. Doucet, M. West, S. Godsill, 2001, Annals of the Institute of Statistical Mathematics.

A particle filter to mitigate jamming for GPS navigation

A. Doucet, J. Tourneret, A. Giremus, 2005, IEEE/SP 13th Workshop on Statistical Signal Processing, 2005.

Gibbs flow for approximate transport with applications to Bayesian computation

A. Doucet, J. Heng, Y. Pokern, 2015, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Invertible Flow Non Equilibrium sampling

Sylvain Le Corff, A. Doucet, É. Moulines, 2021, 2103.10943.

Discussion on the paper by Brooks, Giudici and Roberts

Petros Dellaportas, Christophe Andrieu, Arnaud Doucet, 2003 .

Robust Predictive Uncertainty for Neural Networks via Confidence Densities

A. Doucet, George Deligiannidis, R. Cornish, 2019 .

Asymptotic Properties of Recursive Maximum Likelihood Estimation in Non-Linear State-Space Models

A. Doucet, V. Tadić, 2018, 1806.09571.

Bayesian Approaches to Multi-Sensor Data Fusion

Paul J. Walmsley, A. Doucet, O. Punska, 1999 .

Grouping Priors and the Bayesian Elastic Net

A. Doucet, L. Bornn, R. Gottardo, 2010, 1001.4083.

Sequential simulation-based estimation of jump Markov linear systems

N. Gordon, A. Doucet, V. Krishnamurthy, 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

Particle filters for stochastic differential equations of nonlinear diffusions

A. Doucet, W. Fitzgerald, D. Rimmer, 2005 .

NON-LINEAR MARKOV CHAIN MONTE CARLO

P. Moral, A. Doucet, C. Andrieu, 2007 .

On nonlinear Markov chain Monte Carlo

P. Moral, A. Doucet, C. Andrieu, 2011, Bernoulli.

Blind marginal maximum a posteriori sequence estimation for finite impulse response channels

A. Doucet, C. Andrieu, 1998, Ninth IEEE Signal Processing Workshop on Statistical Signal and Array Processing (Cat. No.98TH8381).

Adaptive MAP multi-user detection for fading CDMA channels

A. Doucet, C. Andrieu, A. Touzni, 2000, Proceedings of the Tenth IEEE Workshop on Statistical Signal and Array Processing (Cat. No.00TH8496).

Particle filtering for multiuser detection in fading CDMA channels

A. Doucet, C. Andrieu, E. Punskaya, 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows

Anthony L. Caterini, A. Doucet, George Deligiannidis, 2019, ICML.

Ensemble Rejection Sampling

A. Doucet, George Deligiannidis, S. Rubenthaler, 2020, 2001.09188.

Particle Motions in Absorbing Medium with Hard and Soft Obstacles

A. Doucet, P. Del Moral, 2004 .

Sequential Monte Carlo methods for diffusion processes

A. Doucet, A. Jasra, 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

Bayesian nonparametric image segmentation using a generalized Swendsen-Wang algorithm

Arnaud Doucet, Richard Yi Da Xu, Francois Caron, 2016, 1602.03048.

European Signal Processing Conference

Christophe Andrieu, Arnaud Doucet, Manuel Davy, 2015 .

An introduction to the theory and applications of simulation based computational methods in Bayesian

Simon J. Godsill, Christophe Andrieu, Arnaud Doucet, 1998 .

Sparse Bayesian Learning for Regression and Classification using Markov Chain Monte Carlo

Kotagiri Ramamohanarao, Arnaud Doucet, Shien-Shin Tham, 2002, ICML.

RADIAL BASIS FUNCTION REGRESSION USING TRANS-DIMENSIONAL SEQUENTIAL

A. Doucet, S. Godsill, J. Vermaak, 2003 .

Simulation-based computational methods for Bayesian signal processing

Patrick Duvaut, Simon J. Godsill, Christophe Andrieu, 1998 .

Joint target tracking and identification-Part I: sequential Monte Carlo model-based approaches

P. Minvielle, A. Doucet, S. Maskell, 2005, 2005 7th International Conference on Information Fusion.

Pseudo-Marginal Hamiltonian Monte Carlo

Fredrik Lindsten, Arnaud Doucet, A. Doucet, 2016, J. Mach. Learn. Res..

Simulation of the Annual Loss Distribution in Operational Risk Via Panjer Recursions and Volterra Integral Equations for Value at Risk and Expected Shortfall Estimation.

A. Doucet, G. Peters, A. M. Johansen, 2017 .

Schrödinger Bridge Samplers

Arnaud Doucet, Espen Bernton, Jeremy Heng, 2021 .

Schr\"odinger Bridge Samplers.

Arnaud Doucet, Pierre E. Jacob, Espen Bernton, 2019, 1912.13170.

Simulation of the annual loss distribution in operational risk via Panjer recursions and Volterra integral equations for value-at-risk and expected shortfall estimation

A. Doucet, G. Peters, A. M. Johansen, 2007 .

Particle Markov Chain Monte Carlo for Multiple Change-point Problems

A. Doucet, C. Andrieu, N. Whiteley, 2009 .

A new class of interacting Markov chain Monte Carlo methods

P. Moral, A. Doucet, 2010 .

On the use and misuse of particle ltering in digital communications

A. Doucet, E. Punskaya, W. Fitzgerald, 2002 .

Stochastic approximation for optimal observer trajectory planning

Robin J. Evans, Ba-Ngu Vo, Arnaud Doucet, 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

A Note on Convergence of the Equi-Energy Sampler

Christophe Andrieu, Arnaud Doucet, Ajay Jasra, 2007, 0711.0164.

PR ] 1 A ug 2 01 8 Stability of Optimal Filter Higher-Order Derivatives

A. Doucet, V. Tadić, 2018 .

Exponential forgetting and geometric ergodicity in state-space models

A. Doucet, V. Tadić, V.B. Tadic, 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

Perfect simulation using atomic regeneration with application to Sequential Monte Carlo

Arnaud Doucet, Anthony Lee, Krzysztof Latuszy'nski, 2014, 1407.5770.

An Adaptive Subsampling Approach for MCMC Inference in Large Datasets

Arnaud Doucet, Chris Holmes, Rémi Bardenet, 2014 .

On nonlinear Markov chain Monte Carlo via Self-interacting approximations.

P. Moral, A. Doucet, C. Andrieu, 2011 .

Metropolis-Hastings with Averaged Acceptance Ratios

A. Doucet, C. Andrieu, N. Chopin, 2020, 2101.01253.

Particle Filtering in Digital Communications

A. Doucet, E. Punskaya, W. Fitzgerald, 2007 .

Exponential forgetting and geo-metric ergodicity in general state-space models

Arnaud Doucet, Vladislav B. Tadić, A. Doucet, 2002 .

Simulated likelihood inference for stochastic volatility models using continuous particle filtering

A. Doucet, M. Pitt, Sheheryar Malik, 2014 .

One-line Parameter Estimation in General State-Space Models using a Pseudo-Likelihood Approach

A. Doucet, V. Tadić, C. Andrieu, 2012 .

Robust inference on parameters via particle filters and sandwich covariance matrices

A. Doucet, N. Shephard, 2012 .

Replica Conditional Sequential Monte

Alexander Y. Shestopaloff, A. Doucet, Carlo, 2019 .

Multivariate Stochastic Volatility with Co-Heteroscedasticity

Joshua C. C. Chan, Rodney W. Strachan, A. Doucet, 2018 .

neural compression workshop COIN : CO MPRESSION WITH I MPLICIT N EURAL REPRESENTATIONS

Y. Teh, A. Doucet, Emilien Dupont, 2021 .

Stability of sequential Monte Carlo samplers via the Foster-Lyapunov condition

Arnaud Doucet, Ajay Jasra, A. Doucet, 2008 .

Limit theorems for sequential MCMC methods

Arnaud Doucet, Adam M. Johansen, Axel Finke, 2018, Advances in Applied Probability.

Methodology for Monte Carlo smoothing with application to time-varying autoregressions

Simon J. Godsill, Michael A. West, Arnaud Doucet, 2000 .

Non‐reversible parallel tempering: A scalable highly parallel MCMC scheme

A. Doucet, George Deligiannidis, A. Bouchard-Côté, 2019, 1905.02939.

Distributed nonlinear consensus in the space of probability measures

A. Doucet, A. Bishop, 2014 .

On uncertainty quantification in hydrogeology and hydrogeophysics

James Irving, Fabio Nobile, Niklas Linde, 2017 .

A Note on E ¢ cient Conditional Simulation of Gaussian Distributions

Arnaud Doucet, A. Doucet, 2010 .

On the conditional distributions of spatial point processes

P. Moral, A. Doucet, F. Caron, 2011, Advances in Applied Probability.

Learning Deep Features in Instrumental Variable Regression pdf

Nando de Freitas, Arthur Gretton, Liyuan Xu, 2020, ICLR.

Particle approximations of a class of branching distribution flows arising in multi-target tracking

P. Moral, A. Doucet, F. Caron, 2010 .

Bayesian model selection of autoregressive processes

Simon J. Godsill, Christophe Andrieu, Arnaud Doucet, 2000 .

Bayesian Computational Approaches to Model Selection

Christophe Andrieu, Arnaud Doucet, William J. Fitzgerald, 2000 .

A Continuous Time Framework for Discrete Denoising Models

Valentin De Bortoli, A. Doucet, George Deligiannidis, 2022, Neural Information Processing Systems.

Riemannian Score-Based Generative Modeling

Valentin De Bortoli, Y. Teh, A. Doucet, 2022, ArXiv.

Chained Generalisation Bounds

A. Doucet, George Deligiannidis, Eugenio Clerico, 2022, COLT.

Online Variational Filtering and Parameter Learning

A. Doucet, Tom Rainforth, Yuyang Shi, 2021, NeurIPS.

Sequential Inference and Learning

Nando de Freitas, Mahesan Niranjan, Arnaud Doucet, 1998, NIPS 1998.

Ranking in Contextual Multi-Armed Bandits

A. Doucet, George Deligiannidis, Amitis Shidani, 2022, 2207.00109.

An Empirical Study of Implicit Regularization in Deep Offline RL

Caglar Gulcehre, Georg Ostrovski, A. Doucet, 2022, Trans. Mach. Learn. Res..

Towards Learning Universal Hyperparameter Optimizers with Transformers

Marc'Aurelio Ranzato, A. Doucet, N. D. Freitas, 2022, ArXiv.

ANNEALED IMPORTANCE SAMPLING MEETS SCORE MATCHING

Will Grathwohl, A. Doucet, Heiko Strathmann, 2022 .

Conditional Simulation Using Diffusion Schrödinger Bridges

Valentin De Bortoli, A. Doucet, George Deligiannidis, 2022, UAI.

Sequential Monte Carlo samplers

P. Moral, A. Doucet, 2002, cond-mat/0212648.

Bayesian models selection approaches to model selection

Arnaud Doucet, Wj Fitzgerald, Arnaud Doucet, 2001 .

The Correlated Pseudo-Marginal Method

A. Doucet, George Deligiannidis, M. Pitt, 2018 .

Importance Weighting Approach in Kernel Bayes' Rule

A. Doucet, A. Gretton, Yutian Chen, 2022, ArXiv.

Score-Based Diffusion meets Annealed Importance Sampling

Will Grathwohl, A. Doucet, Heiko Strathmann, 2022, ArXiv.

Conformal Off-Policy Prediction in Contextual Bandits

Y. Teh, A. Doucet, Jean-Francois Ton, 2022, ArXiv.

A PAC-Bayes bound for deterministic classifiers

A. Doucet, George Deligiannidis, Benjamin Guedj, 2022, ArXiv.

Continual Repeated Annealed Flow Transport Monte Carlo

Danilo Jimenez Rezende, A. Doucet, A. G. D. G. Matthews, 2022, International Conference on Machine Learning.

Stability of Optimal Filter Higher-Order Derivatives

A. Doucet, V. Tadić, 2018, 2019 IEEE 58th Conference on Decision and Control (CDC).

Sequential Monte Carlo & genetic particle models. Theory and practice

Arnaud Doucet, Pierre Del Moral, P. Moral, 2012 .

Semi-supervised learning scheme using Dirichlet process EM-algorithm (パターン認識・メディア理解)

Arnaud Doucet, Yohei Nakada, Tomoaki Kimura, 2009 .

An introduction to the theory and applications of simulation based computational methods in Bayesian signal processing

Simon J. Godsill, Christophe Andrieu, Arnaud Doucet, 1998 .

Sequential Monte Carlo methods for Bayesian computation

Arnaud Doucet, Pierre Del Moral, Ajay Jasra, 2006 .

Convergence of the equi-energy sampler

Christophe Andrieu, Arnaud Doucet, Pierre Del Moral, 2007 .

Riemannian Diffusion Schrödinger Bridge

Valentin De Bortoli, Y. Teh, A. Doucet, 2022, ArXiv.

Generalized Polya Urn for Time-varying Dirichlet Process Mixtures

A. Doucet, M. Davy, F. Caron, 2007, UAI.

Spectral Diffusion Processes

Valentin De Bortoli, A. Doucet, Emile Mathieu, 2022, 2209.14125.

Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows

Valentin De Bortoli, A. Doucet, A. M. Johansen, 2022, ArXiv.

Mitigating statistical bias within differentially private synthetic data

Chris C. Holmes, A. Doucet, Sandra Jeanne Vollmer, 2021, UAI.

Conditional Simulation Using Diffusion Schrödinger Bridges

Valentin De Bortoli, A. Doucet, George Deligiannidis, 2022, Conference on Uncertainty in Artificial Intelligence.

Learning Optimal Conformal Classifiers

A. Doucet, Krishnamurthy Dvijotham, A. Cemgil, 2021, ICLR.

Conformal Off-Policy Prediction in Contextual Bandits

Y. Teh, A. Doucet, Jean-Francois Ton, 2022, NeurIPS.

A Continuous Time Framework for Discrete Denoising Models

Valentin De Bortoli, A. Doucet, George Deligiannidis, 2022, NeurIPS.

Categorical SDEs with Simplex Diffusion

Pierre H. Richemond, A. Doucet, S. Dieleman, 2022, ArXiv.

A PAC-Bayesian bound for deterministic classiﬁers

A. Doucet, George Deligiannidis, Benjamin Guedj, 2022 .

Wide stochastic networks: Gaussian limit and PAC-Bayesian training

A. Doucet, George Deligiannidis, Eugenio Clerico, 2021, ArXiv.

Monte Carlo methods for signal processing

Xiaodong Wang, Arnaud Doucet, A. Doucet, 2005 .

Simulating Diffusion Bridges with Score Matching

Valentin De Bortoli, A. Doucet, J. Heng, 2021, ArXiv.

Maximum Likelihood Learning of Energy-Based Models for Simulation-Based Inference

A. Doucet, A. Gretton, M. Arbel, 2022, ArXiv.

Towards Learning Universal Hyperparameter Optimizers with Transformers

Nando de Freitas, Marc'Aurelio Ranzato, A. Doucet, 2022, NeurIPS.

COIN++: Neural Compression Across Modalities

Y. Teh, A. Doucet, Emilien Dupont, 2022, 2201.12904.

Score-Based Diffusion meets Annealed Importance Sampling

Will Grathwohl, A. Doucet, Heiko Strathmann, 2022, NeurIPS.

Continuous diffusion for categorical data

Pierre H. Richemond, Rémi Leblond, Will Grathwohl, 2022, ArXiv.

On Instrumental Variable Regression for Deep Offline Policy Evaluation

Nando de Freitas, T. Paine, Caglar Gulcehre, 2021, J. Mach. Learn. Res..

Riemannian Score-Based Generative Modeling

Valentin De Bortoli, Y. Teh, A. Doucet, 2022, NeurIPS.

Continuous diffusion for categorical data

Pierre H. Richemond, Rémi Leblond, Will Grathwohl, 2022, ArXiv.

Spectral Diffusion Processes

Valentin De Bortoli, A. Doucet, Emile Mathieu, 2022, ArXiv.

On-line changepoint detection and parameter estimation with application to genomic data

A. Doucet, F. Caron, R. Gottardo, 2011, Statistics and Computing.

Continuous diffusion for categorical data

Pierre H. Richemond, Rémi Leblond, Will Grathwohl, 2022, ArXiv.

Computational Advances for and from Bayesian Analysis

A. Doucet, C. Robert, C. Andrieu, 2004 .

From Denoising Diffusions to Denoising Markov Models

Valentin De Bortoli, A. Doucet, George Deligiannidis, 2022, ArXiv.

Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates

A. Doucet, George Deligiannidis, D. Paulin, 2018, The Annals of Applied Probability.

Derivative-Free Estimation of the Score Vector and Observed Information Matrix with Application to State-Space Models

A. Doucet, P. Jacob, S. Rubenthaler, 2013, 1304.5768.

Particle methods for Bayesian modeling and enhancement of speech signals

Simon J. Godsill, Christophe Andrieu, Arnaud Doucet, 2002, IEEE Trans. Speech Audio Process..

Monte Carlo smoothing with application to audio signal enhancement

Simon J. Godsill, W. Fong, A. Doucet, 2001, Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563).

An adaptive sequential Monte Carlo method for approximate Bayesian computation

Arnaud Doucet, Pierre Del Moral, Ajay Jasra, 2011, Statistics and Computing.

Causal Falsification of Digital Twins

A. Doucet, R. Cornish, C. Holmes, 2023, ArXiv.

Simulated annealing for maximum a Posteriori parameter estimation of hidden Markov models

Christophe Andrieu, Arnaud Doucet, A. Doucet, 2000, IEEE Trans. Inf. Theory.

Stochastic sampling algorithms for state estimation of jump Markov linear systems

Arnaud Doucet, Vikram Krishnamurthy, Andrew Logothetis, 2000, IEEE Trans. Autom. Control..

Generalized Pólya Urn for Time-Varying Pitman-Yor Processes

Arnaud Doucet, Frank D. Wood, Willie Neiswanger, 2017, J. Mach. Learn. Res..

A Bayesian approach to joint tracking and identification of geometric shapes in video sequences

Arnaud Doucet, Simon Maskell, Pierre Minvielle, 2010, Image Vis. Comput..

Sequential Sampling for Dynamic Environment Map Illumination

Wolfgang Heidrich, Arnaud Doucet, Abhijeet Ghosh, 2022 .

Particle methods for change detection, system identification, and control

Christophe Andrieu, Arnaud Doucet, Sumeetpal S. Singh, 2004, Proceedings of the IEEE.

Particle Filtering for Joint Symbol and Code Delay Estimation in DS Spread Spectrum Systems in Multipath Environment

Arnaud Doucet, William J. Fitzgerald, Elena Punskaya, 2004, EURASIP J. Adv. Signal Process..

Special Issue on Particle Filtering in Signal Processing

Petar M. Djuri, Arnaud Doucet, Simon J. Godsill, 2004 .

Nonlinear filtering approaches for INS/GPS integration

Jean-Yves Tourneret, Arnaud Doucet, Audrey Giremus, 2004, 2004 12th European Signal Processing Conference.

Fixed-lag sequential Monte Carlo

Arnaud Doucet, Stéphane Sénécal, A. Doucet, 2004, 2004 12th European Signal Processing Conference.

Sequential Bayesian Kernel Regression

Simon J. Godsill, Arnaud Doucet, Jaco Vermaak, 2003, NIPS.

Particle filtering for demodulation in fading channels with non-Gaussian additive noise

Christophe Andrieu, Arnaud Doucet, William J. Fitzgerald, 2001, IEEE Trans. Commun..

Particle filters for state estimation of jump Markov linear systems

Arnaud Doucet, Neil J. Gordon, Vikram Krishnamurthy, 2001, IEEE Trans. Signal Process..

Particle filtering for multi-target tracking using jump Markov systems

A. Ooi, Ba-Ngu Vo, A. Doucet, 2004, Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004..

Probability hypothesis density filter versus multiple hypothesis tracking

Ba-Ngu Vo, Arnaud Doucet, Kusha Panta, 2004, SPIE Defense + Commercial Sensing.

Markov chain Monte Carlo methods for tracking a maneuvering target in clutter

A. Logothetis, A. Doucet, A. Doucet, 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains

P. Fearnhead, A. Doucet, G. Roberts, 2017, 1701.04244.

Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling

Valentin De Bortoli, A. Doucet, J. Heng, 2021, NeurIPS.

Toward Practical N2 Monte Carlo: the Marginal Particle Filter pdf

Nando de Freitas, Arnaud Doucet, Mike Klaas, 2005, UAI.

An Adaptive Interacting Wang–Landau Algorithm for Automatic Density Exploration

Arnaud Doucet, Luke Bornn, Pierre E. Jacob, 2011, 1109.3829.

Bayesian Phylogenetic Inference Using a Combinatorial Sequential Monte Carlo Method

Arnaud Doucet, Alexandre Bouchard-Côté, A. Doucet, 2015 .

SE(3) diffusion model with application to protein backbone generation

Valentin De Bortoli, T. Jaakkola, A. Doucet, 2023, ArXiv.

Efficient Bayesian Inference for Generalized Bradley–Terry Models

Arnaud Doucet, Francois Caron, A. Doucet, 2010, 1011.1761.

Asynchronous Anytime Sequential Monte Carlo

Yee Whye Teh, Arnaud Doucet, Frank D. Wood, 2014, NIPS.

Sequentially interacting Markov chain Monte Carlo methods

Arnaud Doucet, Pierre Del Moral, Anthony Brockwell, 2010, 1211.2582.

Learning Deep Features in Instrumental Variable Regression pdf

Nando de Freitas, Arnaud Doucet, Arthur Gretton, 2020, ICLR.

Inference for Lévy‐Driven Stochastic Volatility Models via Adaptive Sequential Monte Carlo

Arnaud Doucet, Ajay Jasra, David A. Stephens, 2011 .

A Framework for Kernel-Based Multi-Category Classification

Arnaud Doucet, Simon I. Hill, A. Doucet, 2007, J. Artif. Intell. Res..

Adapting two-class support vector classification methods to many class problems

Arnaud Doucet, Simon I. Hill, A. Doucet, 2005, ICML.

Turning off the “ self-destruction ” mechanism of a cell

Bickel, A. Doucet, A. Jasra, 2012 .

Expectation Particle Belief Propagation

Yee Whye Teh, Arnaud Doucet, Thibaut Lienart, 2015, NIPS.

Asymptotic bias of stochastic gradient search

Arnaud Doucet, Vladislav B. Tadic, A. Doucet, 2011, IEEE Conference on Decision and Control and European Control Conference.

Expectation-maximization algorithms for inference in Dirichlet processes mixture

Arnaud Doucet, T. Kimura, T. Matsumoto, 2011, Pattern Analysis and Applications.

Bayesian Sparsity-Path-Analysis of Genetic Association Signal using Generalized t Priors

Arnaud Doucet, Chris Holmes, Anthony Lee, 2011, Statistical applications in genetics and molecular biology.

Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions

Christophe Andrieu, Arnaud Doucet, Manuel Davy, 2003, IEEE Trans. Signal Process..

COIN: COmpression with Implicit Neural representations pdf

Yee Whye Teh, Arnaud Doucet, Emilien Dupont, 2021, ICLR 2021.

An overview of sequential Monte Carlo methods for parameter estimation in general state-space models

Arnaud Doucet, Jan M. Maciejowski, Nikolas Kantas, 2009 .

Convergence of Sequential Monte Carlo Methods

Arnaud Doucet, Dan Crisan, A. Doucet, 2007 .

Optimal Estimation of Amplitude and Phase Modulated Signals

Christophe Andrieu, Arnaud Doucet, A. Doucet, 2001, Monte Carlo Methods Appl..

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

R. Fergus, Will Grathwohl, J. Tenenbaum, 2023, ArXiv.

NEO: Non Equilibrium Sampling on the Orbit of a Deterministic Transform

Sylvain Le Corff, A. Doucet, É. Moulines, 2021, 2103.10943.

Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding

Chris J. Maddison, A. Doucet, Alireza Makhzani, 2021, ICML.

Continual Repeated Annealed Flow Transport Monte Carlo

Danilo Jimenez Rezende, A. Doucet, A. G. D. G. Matthews, 2022, ICML.

Fast particle smoothing: if I had a million particles

Nando de Freitas, Arnaud Doucet, Mike Klaas, 2006, ICML.

A Particle Method for Solving Fredholm Equations of the First Kind

A. Doucet, A. M. Johansen, F. R. Crucinio, 2020, 2009.09974.

Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models

Arnaud Doucet, Alexandre Bouchard-Côté, Andrew Roth, 2015, J. Mach. Learn. Res..

Gradient-free maximum likelihood parameter estimation with particle filters

A. Doucet, G. Poyiadjis, S. Singh, 2006, 2006 American Control Conference.

Model selection by MCMC computation

Petar M. Djuric, Christophe Andrieu, Arnaud Doucet, 2001, Signal Process..

Bayesian segmentation of piecewise constant autoregressive processes using MCMC methods

O Punska, C Andrieu, Arnaud Doucet, 1999 .

Particle methods for optimal filter derivative: application to parameter estimation

Arnaud Doucet, Sumeetpal S. Singh, George Poyiadjis, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

On embedded hidden Markov models and particle Markov chain Monte Carlo methods

Arnaud Doucet, Adam M. Johansen, Axel Finke, 2016, 1610.08962.

Particle Filtering and Smoothing: Fifteen years later

Arnaud Doucet, Adam M. Johansen, A. Doucet, 2008 .

Inference of geostatistical hyperparameters with the correlated pseudo-marginal method

A. Doucet, D. Ginsbourger, N. Linde, 2023, Advances in Water Resources.

Lithological tomography with the correlated pseudo-marginal method

Arnaud Doucet, David Ginsbourger, Niklas Linde, 2021, Geophysical Journal International.

Diffusion Schr\"odinger Bridge Matching

Valentin De Bortoli, A. Doucet, Yuyang Shi, 2023, 2303.16852.

A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs

Chris C. Holmes, A. Doucet, George Deligiannidis, 2023, NeurIPS.

Generative Models as Distributions of Functions

Y. Teh, A. Doucet, Emilien Dupont, 2021, AISTATS.

COIN++: Neural Compression Across Modalities

Y. Teh, A. Doucet, Emilien Dupont, 2022, Trans. Mach. Learn. Res..

COIN++: Data Agnostic Neural Compression

Y. Teh, A. Doucet, Emilien Dupont, 2022, ArXiv.

From Denoising Diffusions to Denoising Markov Models

Valentin De Bortoli, A. Doucet, George Deligiannidis, 2022, ArXiv.

On the Selection of Initialization and Activation Function for Deep Neural Networks pdf

Arnaud Doucet, Judith Rousseau, Soufiane Hayou, 2018, ArXiv.

Annealed Flow Transport Monte Carlo pdf

Arnaud Doucet, Michael Arbel, Alexander G. D. G. Matthews, 2021, ICML.

Denoising Diffusion Samplers

Will Grathwohl, A. Doucet, Francisco Vargas, 2023, ICLR.

Monte Carlo Variational Auto-Encoders

Eric Moulines, Arnaud Doucet, Alain Durmus, 2021, ICML.

SE(3) diffusion model with application to protein backbone generation

Valentin De Bortoli, Brian L. Trippe, T. Jaakkola, 2023, ICML.

A Unified Framework for U-Net Design and Analysis

Chris C. Holmes, A. Doucet, George Deligiannidis, 2023, NeurIPS.

Sequential Monte Carlo methods for optimisation of neural network models

Arnaud Doucet, Mahesan Niranjan, Jfg de Freitas, 1998 .

Bayesian filtering for hidden Markov models via Monte Carlo methods

Christophe Andrieu, Arnaud Doucet, William J. Fitzgerald, 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

Differentiable Particle Filtering via Entropy-Regularized Optimal Transport

A. Doucet, George Deligiannidis, Adrien Corenflos, 2021, ICML.

Unbiased Smoothing using Particle Independent Metropolis-Hastings

Arnaud Doucet, Pierre E. Jacob, George Deligiannidis, 2019, AISTATS.

Large-sample asymptotics of the pseudo-marginal method

Arnaud Doucet, Michael K. Pitt, George Deligiannidis, 2018, Biometrika.

Filtering Variational Objectives

Yee Whye Teh, Arnaud Doucet, Mohammad Norouzi, 2017, NIPS.

Sequential Monte Carlo Methods

Arnaud Doucet, A. Doucet, 2006, Handbook of Graphical Models.

On the Utility of Graphics Cards to Perform Massively Parallel Simulation of Advanced Monte Carlo Methods

Arnaud Doucet, Anthony Lee, Christopher Yau, 2009, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

Modular Meta-Learning with Shrinkage

Matthew W. Hoffman, Nando de Freitas, Feryal M. P. Behbahani, 2019, NeurIPS.

Distributed Maximum Likelihood for Simultaneous Self-Localization and Tracking in Sensor Networks

Arnaud Doucet, Sumeetpal S. Singh, Nikolaos Kantas, 2012, IEEE Transactions on Signal Processing.

Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting

Daniel Paulin, Maxime Vono, Arnaud Doucet, 2019, J. Mach. Learn. Res..

Error Bounds for Flow Matching Methods

A. Doucet, George Deligiannidis, Joe Benton, 2023, ArXiv.

Robust Pruning at Initialization

Y. Teh, A. Doucet, Jean-Francois Ton, 2020, ICLR.

SMC Samplers for Bayesian Optimal Nonlinear Design

Arnaud Doucet, Hendrik Kuck, Nando de Freitas, 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.

On the utility of Metropolis-Hastings with asymmetric acceptance ratio

Christophe Andrieu, Arnaud Doucet, Sinan Yildirim, 2018, 1803.09527.

Monte Carlo filtering and smoothing with application to time-varying spectral estimation

Simon J. Godsill, Arnaud Doucet, Mike West, 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

Trans-Dimensional Generative Modeling via Jump Diffusion Models

Valentin De Bortoli, A. Doucet, Tom Rainforth, 2023, ArXiv.

Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC

R. Fergus, Will Grathwohl, J. Tenenbaum, 2023, ICML.

Tree-Based Diffusion Schrödinger Bridge with Applications to Wasserstein Barycenters

Valentin De Bortoli, A. Doucet, Alain Durmus, 2023, ArXiv.

Diffusion Schrödinger Bridge Matching

Valentin De Bortoli, A. Doucet, Yuyang Shi, 2023, NeurIPS.

Hamiltonian Variational Auto-Encoder

Arnaud Doucet, Dino Sejdinovic, Anthony L. Caterini, 2018, NeurIPS.

A new class of soft MIMO demodulation algorithms

Bin Dong, Arnaud Doucet, Xiaodong Wang, 2003, IEEE Trans. Signal Process..

Particle methods for maximum likelihood estimation in latent variable models

Arnaud Doucet, Adam M. Johansen, Manuel Davy, 2008, Stat. Comput..

Simulation-based Methods for Blind Maximum-likelihood Filter Identiication Simulation-based Methods for Blind Maximum-likelihood Filter Identiication

A. Doucet, É. Moulines, M. Lavielle, 2008 .

Generalisation under gradient descent via deterministic PAC-Bayes

A. Doucet, George Deligiannidis, Benjamin Guedj, 2022, 2209.02525.

Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling

Arnaud Doucet, Jean-François Giovannelli, François Caron, 2017, NIPS.

Autoregressive Kernels For Time Series

Arnaud Doucet, Marco Cuturi, A. Doucet, 2011, 1101.0673.

Wide stochastic networks: Gaussian limit and PAC-Bayesian training

A. Doucet, George Deligiannidis, Eugenio Clerico, 2021, ALT.

Stable ResNet

A. Doucet, George Deligiannidis, J. Rousseau, 2020, AISTATS.

Mean-field Behaviour of Neural Tangent Kernel for Deep Neural Networks

A. Doucet, J. Rousseau, Soufiane Hayou, 2019 .

Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel pdf

Arnaud Doucet, Judith Rousseau, Soufiane Hayou, 2019, ArXiv.

Exact Convergence Rates of the Neural Tangent Kernel in the Large Depth Limit

Arnaud Doucet, Judith Rousseau, Soufiane Hayou, 2019, 1905.13654.

On the Impact of the Activation Function on Deep Neural Networks Training pdf

Arnaud Doucet, Judith Rousseau, Soufiane Hayou, 2019, ICML.

Conditionally Gaussian PAC-Bayes

A. Doucet, George Deligiannidis, Eugenio Clerico, 2021, AISTATS.

Conditional Gaussian PAC-Bayes pdf

Arnaud Doucet, George Deligiannidis, Eugenio Clerico, 2021, ArXiv.

Pruning untrained neural networks: Principles and Analysis pdf

Yee Whye Teh, Arnaud Doucet, Jean-Francois Ton, 2020, ArXiv.

Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale pdf

Arnaud Doucet, Sebastian Vollmer, Harrison Wilde, 2021, ArXiv.

Maximum Likelihood Learning of Unnormalized Models for Simulation-Based Inference

A. Doucet, A. Gretton, M. Arbel, 2022, 2210.14756.

Optimized support vector machines for nonstationary signal classification

P.J.W. Rayner, A. Doucet, M. Davy, 2002, IEEE Signal Processing Letters.

An improved method for uniform simulation of stable minimum phase real ARMA (p,q) processes

Christophe Andrieu, Arnaud Doucet, A. Doucet, 1999, IEEE Signal Processing Letters.

Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure pdf

Arnaud Doucet, Valentin De Bortoli, George Deligiannidis, 2021, ArXiv.

Learning Deep Features in Instrumental Variable Regression

Nando de Freitas, A. Doucet, N. D. Freitas, 2020, ICLR.

Conformal prediction under ambiguous ground truth

Abhijit Guha Roy, A. Doucet, A. Cemgil, 2023, 2307.09302.

Evaluating AI systems under uncertain ground truth: a case study in dermatology

Abhijit Guha Roy, A. Doucet, Pushmeet Kohli, 2023, ArXiv.

Blind SOS subspace channel estimation and equalization techniques exploiting spatial diversity in OFDM systems

Arnaud Doucet, Yingbo Hua, Hassan Ali, 2004, Digit. Signal Process..

On PAC-Bayesian reconstruction guarantees for VAEs

A. Doucet, Benjamin Guedj, Badr-Eddine Ch'erief-Abdellatif, 2022, AISTATS.

On-line changepoint detection and parameter estimation with application to genomic data

Raphael Gottardo, Arnaud Doucet, François Caron, 2012, Stat. Comput..

THE UNIVERSITY OF BRITISH COLUMBIA DEPARTMENT OF STATISTICS TECHNICAL REPORT # 236 On-line Changepoint Detection and Parameter Estimation for Genome-wide Transcript Analysis

A. Doucet, F. Caron, R. Gottardo, 2007 .

A policy gradient method for SMDPs with application to call admission control

Arnaud Doucet, Vladislav B. Tadic, Sumetpal Singh, 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

Expectation-maximization algorithms for inference in Dirichlet processes mixture

A. Doucet, T. Matsumoto, T. Tokuda, 2013, Pattern Analysis and Applications.

Space alternating data augmentation: application to finite mixture of Gaussians and speaker recognition

Arnaud Doucet, Tomoko Matsui, Stéphane Sénécal, 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

Interacting sequential Monte Carlo samplers for trans-dimensional simulation

Arnaud Doucet, Ajay Jasra, Christopher C. Holmes, 2008, Comput. Stat. Data Anal..

NON-LINEAR MARKOV CHAIN MONTE

P. Moral, A. Doucet, C. Andrieu, 2007 .

Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving

A. Doucet, Maximilian Igl, J. Messias, 2022, CoRL.

Trans-dimensional MCMC for Bayesian policy learning

Matthew W. Hoffman, Nando de Freitas, Arnaud Doucet, 2007, NIPS 2007.

Simulation-Based Optimal Sensor Scheduling with Application to Observer Trajectory Planning

Robin J. Evans, Ba-Ngu Vo, Arnaud Doucet, 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

A boosting approach to structure learning of graphs with and without prior knowledge

Arnaud Doucet, Christopher C. Holmes, Shahzia Anjum, 2009, Bioinform..

Reinforced Self-Training (ReST) for Language Modeling

Nando de Freitas, T. Paine, Caglar Gulcehre, 2023, ArXiv.

Localised Generative Flows

Arnaud Doucet, Rob Cornish, George Deligiannidis, 2019, ArXiv.

Bayesian estimation of state-space models applied to deconvolution of Bernoulli - Gaussian processes

Patrick Duvaut, Arnaud Doucet, A. Doucet, 1997, Signal Process..

Linear Convergence Bounds for Diffusion Models via Stochastic Localization

Valentin De Bortoli, A. Doucet, George Deligiannidis, 2023, ArXiv.

Sequential sampling for dynamic environment maps

A. Doucet, W. Heidrich, A. Ghosh, 2006, SIGGRAPH Sketches.

Bayesian Nonparametric Models on Decomposable Graphs

Arnaud Doucet, François Caron, A. Doucet, 2009, NIPS.

An Introduction to MCMC for Machine Learning

Nando de Freitas, Michael I. Jordan, A. Doucet, 2004, Machine Learning.

Copulas: a new insight into positive time-frequency distributions

M. Davy, A. Doucet, M. Davy, 2003, IEEE Signal Processing Letters.

Riemannian Score-Based Generative Modeling

Valentin De Bortoli, Y. Teh, A. Doucet, 2022, ArXiv.

An Introduction to MCMC for Machine Learning

Nando de Freitas, Michael I. Jordan, A. Doucet, 2004, Machine Learning.

Diffusion Schr\"{o}dinger Bridges for Bayesian Computation

Valentin De Bortoli, A. Doucet, J. Heng, 2023, 2308.14106.

Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation

Arnaud Doucet, Vladislav Z. B. Tadić, A. Doucet, 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows

Valentin De Bortoli, A. Doucet, A. M. Johansen, 2022, ArXiv.

Network Consensus in the Wasserstein Metric Space of Probability Measures

A. Doucet, A. Bishop, 2014, SIAM J. Control. Optim..

Particle filters for demodulation of M-ary modulated signals in noisy fading communication channels

Christophe Andrieu, Arnaud Doucet, William J. Fitzgerald, 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

Recursive state estimation for multiple switching models with unknown transition probabilities

Arnaud Doucet, A. Doucet, B. Ristic, 2002 .

Sequential Monte Carlo for maneuvering target tracking in clutter

Arnaud Doucet, Neil J. Gordon, N. Gordon, 1999, Optics & Photonics.

Statistical Applications in Genetics and Molecular Biology Bayesian Sparsity-path-analysis of Genetic Association Signal Using Generalized T Priors Bayesian Sparsity-path-analysis of Genetic Association Signal Using Generalized T Priors

A. Doucet, Anthony Lee, C. Holmes, 2022 .

Particle approximations of the score and observed information matrix in state space models with application to parameter estimation

Sumeetpal S. Singh, A. Doucet, G. Poyiadjis, 2011 .

Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation

Arnaud Doucet, Vladislav Z. B. Tadić, A. Doucet, 2021, IEEE Transactions on Information Theory.

Bias of Particle Approximations to Optimal Filter Derivative

Arnaud Doucet, Vladislav Z.B. Tadic, A. Doucet, 2018, SIAM J. Control. Optim..