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2020 – today
- 2022
[j63]Maxime Vono, Daniel Paulin, Arnaud Doucet:
Efficient MCMC Sampling with Dimension-Free Convergence Rate using ADMM-type Splitting. J. Mach. Learn. Res. 23: 25:1-25:69 (2022)- [c102]Eugenio Clerico, George Deligiannidis, Arnaud Doucet:
Conditionally Gaussian PAC-Bayes. AISTATS 2022: 2311-2329 - [c101]Emilien Dupont, Yee Whye Teh, Arnaud Doucet:
Generative Models as Distributions of Functions. AISTATS 2022: 2989-3015 - [c100]Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj:
On PAC-Bayesian reconstruction guarantees for VAEs. AISTATS 2022: 3066-3079 - [i49]Emilien Dupont, Hrushikesh Loya, Milad Alizadeh, Adam Golinski, Yee Whye Teh, Arnaud Doucet:
COIN++: Data Agnostic Neural Compression. CoRR abs/2201.12904 (2022) - [i48]Alexander G. de G. Matthews, Michael Arbel, Danilo J. Rezende, Arnaud Doucet:
Continual Repeated Annealed Flow Transport Monte Carlo. CoRR abs/2201.13117 (2022) - [i47]Liyuan Xu, Yutian Chen, Arnaud Doucet, Arthur Gretton:
Importance Weighting Approach in Kernel Bayes' Rule. CoRR abs/2202.02474 (2022) - [i46]Valentin De Bortoli, Emile Mathieu, Michael Hutchinson, James Thornton, Yee Whye Teh, Arnaud Doucet:
Riemannian Score-Based Generative Modeling. CoRR abs/2202.02763 (2022) - [i45]Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj:
On PAC-Bayesian reconstruction guarantees for VAEs. CoRR abs/2202.11455 (2022) - [i44]Yuyang Shi, Valentin De Bortoli, George Deligiannidis, Arnaud Doucet:
Conditional Simulation Using Diffusion Schrödinger Bridges. CoRR abs/2202.13460 (2022) - [i43]Eugenio Clerico, Amitis Shidani, George Deligiannidis, Arnaud Doucet:
Chained Generalisation Bounds. CoRR abs/2203.00977 (2022) - [i42]Yutian Chen, Xingyou Song, Chansoo Lee, Zi Wang, Qiuyi Zhang, David Dohan, Kazuya Kawakami, Greg Kochanski, Arnaud Doucet, Marc'Aurelio Ranzato, Sagi Perel, Nando de Freitas:
Towards Learning Universal Hyperparameter Optimizers with Transformers. CoRR abs/2205.13320 (2022) - [i41]Andrew Campbell, Joe Benton, Valentin De Bortoli, Tom Rainforth, George Deligiannidis, Arnaud Doucet:
A Continuous Time Framework for Discrete Denoising Models. CoRR abs/2205.14987 (2022) - [i40]Muhammad Faaiz Taufiq, Jean-Francois Ton, Robert Cornish, Yee Whye Teh, Arnaud Doucet:
Conformal Off-Policy Prediction in Contextual Bandits. CoRR abs/2206.04405 (2022) - 2021
- [j62]Philippe Gagnon
, Arnaud Doucet:
Nonreversible Jump Algorithms for Bayesian Nested Model Selection. J. Comput. Graph. Stat. 30(2): 312-323 (2021) - [j61]Vladislav Z. B. Tadic, Arnaud Doucet:
Bias of Particle Approximations to Optimal Filter Derivative. SIAM J. Control. Optim. 59(1): 727-748 (2021) - [j60]Adrian N. Bishop, Arnaud Doucet:
Network Consensus in the Wasserstein Metric Space of Probability Measures. SIAM J. Control. Optim. 59(5): 3261-3277 (2021) - [j59]Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Arnaud Doucet:
Dual Space Preconditioning for Gradient Descent. SIAM J. Optim. 31(1): 991-1016 (2021) - [j58]Vladislav Z. B. Tadic, Arnaud Doucet:
Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation. IEEE Trans. Inf. Theory 67(3): 1825-1848 (2021) - [c99]Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau:
Stable ResNet. AISTATS 2021: 1324-1332 - [c98]Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh:
Robust Pruning at Initialization. ICLR 2021 - [c97]Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton:
Learning Deep Features in Instrumental Variable Regression. ICLR 2021 - [c96]Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. ICML 2021: 318-330 - [c95]Adrien Corenflos, James Thornton, George Deligiannidis, Arnaud Doucet:
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport. ICML 2021: 2100-2111 - [c94]Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison:
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. ICML 2021: 9136-9147 - [c93]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. ICML 2021: 10247-10257 - [c92]Achille Thin, Yazid Janati El Idrissi, Sylvain Le Corff, Charles Ollion, Eric Moulines, Arnaud Doucet, Alain Durmus, Christian X. Robert:
NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform. NeurIPS 2021: 17060-17071 - [c91]Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet:
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling. NeurIPS 2021: 17695-17709 - [c90]Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet:
Online Variational Filtering and Parameter Learning. NeurIPS 2021: 18633-18645 - [c89]Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational inference with continuously-indexed normalizing flows. UAI 2021: 44-53 - [c88]Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet:
Unbiased gradient estimation for variational auto-encoders using coupled Markov chains. UAI 2021: 707-717 - [i39]Emilien Dupont, Yee Whye Teh, Arnaud Doucet:
Generative Models as Distributions of Functions. CoRR abs/2102.04776 (2021) - [i38]Michael Arbel, Alexander G. de G. Matthews, Arnaud Doucet:
Annealed Flow Transport Monte Carlo. CoRR abs/2102.07501 (2021) - [i37]Adrien Corenflos, James Thornton, Arnaud Doucet, George Deligiannidis:
Differentiable Particle Filtering via Entropy-Regularized Optimal Transport. CoRR abs/2102.07850 (2021) - [i36]Yangjun Ruan, Karen Ullrich, Daniel Severo, James Townsend, Ashish Khisti, Arnaud Doucet, Alireza Makhzani, Chris J. Maddison:
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding. CoRR abs/2102.11086 (2021) - [i35]Emilien Dupont, Adam Golinski, Milad Alizadeh, Yee Whye Teh, Arnaud Doucet:
COIN: COmpression with Implicit Neural representations. CoRR abs/2103.03123 (2021) - [i34]Yutian Chen, Liyuan Xu, Çaglar Gülçehre, Tom Le Paine, Arthur Gretton, Nando de Freitas, Arnaud Doucet:
On Instrumental Variable Regression for Deep Offline Policy Evaluation. CoRR abs/2105.10148 (2021) - [i33]Valentin De Bortoli, James Thornton, Jeremy Heng, Arnaud Doucet:
Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling. CoRR abs/2106.01357 (2021) - [i32]Eugenio Clerico, George Deligiannidis, Arnaud Doucet:
Wide stochastic networks: Gaussian limit and PAC-Bayesian training. CoRR abs/2106.09798 (2021) - [i31]Achille Thin, Nikita Kotelevskii, Arnaud Doucet, Alain Durmus, Eric Moulines, Maxim Panov:
Monte Carlo Variational Auto-Encoders. CoRR abs/2106.15921 (2021) - [i30]George Deligiannidis, Valentin De Bortoli, Arnaud Doucet:
Quantitative Uniform Stability of the Iterative Proportional Fitting Procedure. CoRR abs/2108.08129 (2021) - [i29]Sahra Ghalebikesabi, Harrison Wilde, Jack Jewson, Arnaud Doucet, Sebastian J. Vollmer, Chris C. Holmes:
Bias Mitigated Learning from Differentially Private Synthetic Data: A Cautionary Tale. CoRR abs/2108.10934 (2021) - [i28]David Stutz, Krishnamurthy Dvijotham, Ali Taylan Cemgil, Arnaud Doucet:
Learning Optimal Conformal Classifiers. CoRR abs/2110.09192 (2021) - [i27]Eugenio Clerico, George Deligiannidis, Arnaud Doucet:
Conditional Gaussian PAC-Bayes. CoRR abs/2110.11886 (2021) - [i26]Andrew Campbell, Yuyang Shi, Tom Rainforth, Arnaud Doucet:
Online Variational Filtering and Parameter Learning. CoRR abs/2110.13549 (2021) - [i25]Valentin De Bortoli, Arnaud Doucet, Jeremy Heng, James Thornton:
Simulating Diffusion Bridges with Score Matching. CoRR abs/2111.07243 (2021) - 2020
- [c87]Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet:
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows. ICML 2020: 2133-2143 - [c86]Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas:
Modular Meta-Learning with Shrinkage. NeurIPS 2020 - [i24]Soufiane Hayou, Jean-Francois Ton, Arnaud Doucet, Yee Whye Teh:
Pruning untrained neural networks: Principles and Analysis. CoRR abs/2002.08797 (2020) - [i23]Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational Inference with Continuously-Indexed Normalizing Flows. CoRR abs/2007.05426 (2020) - [i22]Francisco J. R. Ruiz, Michalis K. Titsias, A. Taylan Cemgil, Arnaud Doucet:
Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains. CoRR abs/2010.01845 (2020) - [i21]Liyuan Xu, Yutian Chen, Siddarth Srinivasan, Nando de Freitas, Arnaud Doucet, Arthur Gretton:
Learning Deep Features in Instrumental Variable Regression. CoRR abs/2010.07154 (2020) - [i20]Soufiane Hayou, Eugenio Clerico, Bobby He, George Deligiannidis, Arnaud Doucet, Judith Rousseau:
Stable ResNet. CoRR abs/2010.12859 (2020)
2010 – 2019
- 2019
- [j57]Vladislav Z. B. Tadic, Arnaud Doucet:
Analyticity of Entropy Rates of Continuous-State Hidden Markov Models. IEEE Trans. Inf. Theory 65(12): 7950-7975 (2019) - [c85]Sebastian M. Schmon, Arnaud Doucet, George Deligiannidis:
Bernoulli Race Particle Filters. AISTATS 2019: 2350-2358 - [c84]Lawrece Middleton, George Deligiannidis, Arnaud Doucet, Pierre E. Jacob:
Unbiased Smoothing using Particle Independent Metropolis-Hastings. AISTATS 2019: 2378-2387 - [c83]Vladislav Z. B. Tadic, Arnaud Doucet:
Stability of Optimal Filter Higher-Order Derivatives. CDC 2019: 1644-1649 - [c82]Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet:
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. ICML 2019: 1351-1360 - [c81]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Impact of the Activation function on Deep Neural Networks Training. ICML 2019: 2672-2680 - [c80]Alex Shestopaloff, Arnaud Doucet:
Replica Conditional Sequential Monte Carlo. ICML 2019: 5749-5757 - [c79]Vladislav Z. B. Tadic, Arnaud Doucet:
Asymptotic Properties of Recursive Particle Maximum Likelihood Estimation. ISIT 2019: 887-891 - [c78]Emilien Dupont, Arnaud Doucet, Yee Whye Teh:
Augmented Neural ODEs. NeurIPS 2019: 3134-3144 - [i19]Robert Cornish, Paul Vanetti, Alexandre Bouchard-Côté, George Deligiannidis, Arnaud Doucet:
Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets. CoRR abs/1901.09881 (2019) - [i18]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Impact of the Activation Function on Deep Neural Networks Training. CoRR abs/1902.06853 (2019) - [i17]Emilien Dupont, Arnaud Doucet, Yee Whye Teh:
Augmented Neural ODEs. CoRR abs/1904.01681 (2019) - [i16]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
Training Dynamics of Deep Networks using Stochastic Gradient Descent via Neural Tangent Kernel. CoRR abs/1905.13654 (2019) - [i15]Yutian Chen, Abram L. Friesen, Feryal Behbahani, David Budden, Matthew W. Hoffman, Arnaud Doucet, Nando de Freitas:
Modular Meta-Learning with Shrinkage. CoRR abs/1909.05557 (2019) - [i14]Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet:
Localised Generative Flows. CoRR abs/1909.13833 (2019) - 2018
- [c77]Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. NeurIPS 2018: 8178-8188 - [i13]Soufiane Hayou, Arnaud Doucet, Judith Rousseau:
On the Selection of Initialization and Activation Function for Deep Neural Networks. CoRR abs/1805.08266 (2018) - [i12]Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. CoRR abs/1805.11328 (2018) - [i11]Vladislav Z. B. Tadic, Arnaud Doucet:
Analyticity of Entropy Rates of Continuous-State Hidden Markov Models. CoRR abs/1806.09589 (2018) - [i10]Chris J. Maddison, Daniel Paulin, Yee Whye Teh, Brendan O'Donoghue, Arnaud Doucet:
Hamiltonian Descent Methods. CoRR abs/1809.05042 (2018) - 2017
- [j56]François Caron, Willie Neiswanger, Frank D. Wood, Arnaud Doucet, Manuel Davy:
Generalized Pólya Urn for Time-Varying Pitman-Yor Processes. J. Mach. Learn. Res. 18: 27:1-27:32 (2017) - [j55]Alexandre Bouchard-Côté, Arnaud Doucet, Andrew Roth:
Particle Gibbs Split-Merge Sampling for Bayesian Inference in Mixture Models. J. Mach. Learn. Res. 18: 28:1-28:39 (2017) - [j54]Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
On Markov chain Monte Carlo methods for tall data. J. Mach. Learn. Res. 18: 47:1-47:43 (2017) - [c76]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. ICLR (Workshop) 2017 - [c75]Andrei-Cristian Barbos, Francois Caron, Jean-François Giovannelli, Arnaud Doucet:
Clone MCMC: Parallel High-Dimensional Gaussian Gibbs Sampling. NIPS 2017: 5020-5028 - [c74]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. NIPS 2017: 6573-6583 - [i9]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, Andriy Mnih, Yee Whye Teh:
Particle Value Functions. CoRR abs/1703.05820 (2017) - [i8]Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Mohammad Norouzi, Andriy Mnih, Arnaud Doucet, Yee Whye Teh:
Filtering Variational Objectives. CoRR abs/1705.09279 (2017) - 2016
- [c73]Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood:
Interacting Particle Markov Chain Monte Carlo. ICML 2016: 2616-2625 - 2015
- [j53]Pierre Del Moral, Arnaud Doucet, Sumeetpal S. Singh:
Uniform Stability of a Particle Approximation of the Optimal Filter Derivative. SIAM J. Control. Optim. 53(3): 1278-1304 (2015) - [c72]Thibaut Lienart, Yee Whye Teh, Arnaud Doucet:
Expectation Particle Belief Propagation. NIPS 2015: 3609-3617 - [i7]Thibaut Lienart, Yee Whye Teh, Arnaud Doucet:
Expectation Particle Belief Propagation. CoRR abs/1506.05934 (2015) - 2014
- [j52]Ido Nevat, Gareth W. Peters, Arnaud Doucet, Jinhong Yuan:
Joint Channel and Doppler Offset Estimation in Dynamic Cooperative Relay Networks. IEEE Trans. Wirel. Commun. 13(12): 6570-6579 (2014) - [c71]Rémi Bardenet, Arnaud Doucet, Christopher C. Holmes:
Towards scaling up Markov chain Monte Carlo: an adaptive subsampling approach. ICML 2014: 405-413 - [c70]Marco Cuturi, Arnaud Doucet:
Fast Computation of Wasserstein Barycenters. ICML 2014: 685-693 - [c69]Brooks Paige, Frank D. Wood, Arnaud Doucet, Yee Whye Teh:
Asynchronous Anytime Sequential Monte Carlo. NIPS 2014: 3410-3418 - 2013
- [j51]T. Kimura, T. Tokuda, Y. Nakada, T. Nokajima, T. Matsumoto, Arnaud Doucet:
Expectation-maximization algorithms for inference in Dirichlet processes mixture. Pattern Anal. Appl. 16(1): 55-67 (2013) - [j50]Arnaud Doucet, Christian P. Robert:
Introduction to Special Issue on Monte Carlo Methods in Statistics. ACM Trans. Model. Comput. Simul. 23(1): 1:1-1:2 (2013) - [i6]Christophe Andrieu, Nando de Freitas, Arnaud Doucet:
Reversible Jump MCMC Simulated Annealing for Neural Networks. CoRR abs/1301.3833 (2013) - [i5]Arnaud Doucet, Nando de Freitas, Kevin P. Murphy, Stuart Russell:
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks. CoRR abs/1301.3853 (2013) - 2012
- [j49]Francois Caron, Arnaud Doucet, Raphael Gottardo:
On-line changepoint detection and parameter estimation with application to genomic data. Stat. Comput. 22(2): 579-595 (2012) - [j48]Pierre Del Moral, Arnaud Doucet, Ajay Jasra:
An adaptive sequential Monte Carlo method for approximate Bayesian computation. Stat. Comput. 22(5): 1009-1020 (2012) - [j47]Nikolaos Kantas, Sumeetpal S. Singh, Arnaud Doucet:
Distributed Maximum Likelihood for Simultaneous Self-Localization and Tracking in Sensor Networks. IEEE Trans. Signal Process. 60(10): 5038-5047 (2012) - [i4]Matthias Hoffman, Hendrik Kück, Nando de Freitas, Arnaud Doucet:
New inference strategies for solving Markov Decision Processes using reversible jump MCMC. CoRR abs/1205.2643 (2012) - [i3]Nikolaos Kantas, Sumeetpal S. Singh, Arnaud Doucet:
Distributed Maximum Likelihood for Simultaneous Self-localization and Tracking in Sensor Networks. CoRR abs/1206.4221 (2012) - [i2]Mike Klaas, Nando de Freitas, Arnaud Doucet:
Toward Practical N2 Monte Carlo: the Marginal Particle Filter. CoRR abs/1207.1396 (2012) - 2011
- [j46]Seokhwan Jo, Chang D. Yoo, Arnaud Doucet:
Melody Tracking Based on Sequential Bayesian Model. IEEE J. Sel. Top. Signal Process. 5(6): 1216-1227 (2011) - [j45]Francois Caron, Pierre Del Moral, Arnaud Doucet, Michele Pace:
Particle Approximation of the Intensity Measures of a Spatial Branching Point Process Arising in Multitarget Tracking. SIAM J. Control. Optim. 49(4): 1766-1792 (2011) - [c68]Vladislav Z. B. Tadic, Arnaud Doucet:
Asymptotic bias of stochastic gradient search. CDC/ECC 2011: 722-727 - 2010
- [j44]Arnaud Doucet, Adam M. Johansen, Vladislav Z. B. Tadic:
On solving integral equations using Markov chain Monte Carlo methods. Appl. Math. Comput. 216(10): 2869-2880 (2010) - [j43]Pierre Minvielle, Arnaud Doucet, Alan Marrs, Simon Maskell:
A Bayesian approach to joint tracking and identification of geometric shapes in video sequences. Image Vis. Comput. 28(1): 111-123 (2010) - [j42]Audrey Giremus, Jean-Yves Tourneret, Arnaud Doucet:
A Fixed-Lag Particle Filter for the Joint Detection/Compensation of Interference Effects in GPS Navigation. IEEE Trans. Signal Process. 58(12): 6066-6079 (2010) - [i1]Ido Nevat, Gareth W. Peters, Arnaud Doucet, Jinhong Yuan:
Channel Tracking for Relay Networks via Adaptive Particle MCMC. CoRR abs/1006.3151 (2010)
2000 – 2009
- 2009
- [j41]Ruben Martinez-Cantin, Nando de Freitas, Eric Brochu, José A. Castellanos, Arnaud Doucet:
A Bayesian exploration-exploitation approach for optimal online sensing and planning with a visually guided mobile robot. Auton. Robots 27(2): 93-103 (2009) - [j40]Shahzia Anjum, Arnaud Doucet, Christopher C. Holmes:
A boosting approach to structure learning of graphs with and without prior knowledge. Bioinform. 25(22): 2929-2936 (2009) - [j39]Smita Sadhu, Shovan Bhaumik, Arnaud Doucet, Tapan Kumar Ghoshal:
Particle-method-based formulation of risk-sensitive filter. Signal Process. 89(3): 314-319 (2009) - [c67]Hendrik Kück, Matt Hoffman, Arnaud Doucet, Nando de Freitas:
Inference and Learning for Active Sensing, Experimental Design and Control. IbPRIA 2009: 1-10 - [c66]Francois Caron, Arnaud Doucet:
Bayesian Nonparametric Models on Decomposable Graphs. NIPS 2009: 225-233 - [c65]Matthias Hoffman, Hendrik Kück, Nando de Freitas, Arnaud Doucet:
New inference strategies for solving Markov Decision Processes using reversible jump MCMC. UAI 2009: 223-231 - [c64]Matthew Hoffman, Nando de Freitas, Arnaud Doucet, Jan Peters:
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward. AISTATS 2009: 232-239 - 2008
- [j38]Ajay Jasra, Arnaud Doucet, David A. Stephens, Christopher C. Holmes:
Interacting sequential Monte Carlo samplers for trans-dimensional simulation. Comput. Stat. Data Anal. 52(4): 1765-1791 (2008) - [j37]Adam M. Johansen, Arnaud Doucet, Manuel Davy:
Particle methods for maximum likelihood estimation in latent variable models. Stat. Comput. 18(1): 47-57 (2008) - [j36]Francois Caron, Manuel Davy, Arnaud Doucet, Emmanuel Duflos, Philippe Vanheeghe:
Bayesian Inference for Linear Dynamic Models With Dirichlet Process Mixtures. IEEE Trans. Signal Process. 56(1): 71-84 (2008) - [c63]Francois Caron, Arnaud Doucet:
Sparse Bayesian nonparametric regression. ICML 2008: 88-95 - 2007
- [j35]Sumeetpal S. Singh, Nikolaos Kantas, Ba-Ngu Vo, Arnaud Doucet, Robin J. Evans:
Simulation-based optimal sensor scheduling with application to observer trajectory planning. Autom. 43(5): 817-830 (2007) - [j34]Sumeetpal S. Singh, Vladislav Z. B. Tadic, Arnaud Doucet:
A policy gradient method for semi-Markov decision processes with application to call admission control. Eur. J. Oper. Res. 178(3): 808-818 (2007) - [j33]Simon I. Hill, Arnaud Doucet:
A Framework for Kernel-Based Multi-Category Classification. J. Artif. Intell. Res. 30: 525-564 (2007) - [j32]Hassan Ali, Arnaud Doucet, Dino Isa Amshah:
GSR: A New Genetic Algorithm for Improving Source and Channel Estimates. IEEE Trans. Circuits Syst. I Regul. Pap. 54-I(5): 1088-1098 (2007) - [c62]Edmund S. Jackson, Manuel Davy, Arnaud Doucet, William J. Fitzgerald:
Bayesian Unsupervised Signal Classification by Dirichlet Process Mixtures of Gaussian Processes. ICASSP (3) 2007: 1077-1080 - [c61]Vladislav Z. B. Tadic, Arnaud Doucet:
A Monte Carlo Algorithm for Optimal Quantization in Hidden Markov Models. ISIT 2007: 1121-1125 - [c60]Matt Hoffman, Arnaud Doucet, Nando de Freitas, Ajay Jasra:
Bayesian Policy Learning with Trans-Dimensional MCMC. NIPS 2007: 665-672 - [c59]Ruben Martinez-Cantin, Nando de Freitas, Arnaud Doucet, José A. Castellanos:
Active Policy Learning for Robot Planning and Exploration under Uncertainty. Robotics: Science and Systems 2007 - [c58]Francois Caron, Manuel Davy, Arnaud Doucet:
Generalized Polya Urn for Time-varying Dirichlet Process Mixtures. UAI 2007: 33-40 - 2006
- [c57]Francois Caron, Manuel Davy, Arnaud Doucet, Emmanuel Duflos, Philippe Vanheeghe:
Bayesian Inference for Dynamic Models with Dirichlet Process Mixtures. FUSION 2006: 1-8 - [c56]Nikolaos Kantas, Sumeetpal S. Singh, Arnaud Doucet:
A Distributed Recursive Maximum Likelihood Implementation for Sensor Registration. FUSION 2006: 1-8 - [c55]George Poyiadjis, Sumeetpal S. Singh, Arnaud Doucet:
Particle Filter as A Controlled Markov Chain For On-Line Parameter Estimation in General State Space Models. ICASSP (3) 2006: 329-332 - [c54]