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| - | * **Current position**: Full professor at Telecom Sudparis (since jan. 2008). Part time “Professeur chargé de cours”, Ecole Polytechnique (since 2017). | + | * **Current position**: Full professor at Telecom Sudparis (2008-current). |
| - | * **Former position**: Full time "Professeur chargé de cours", Ecole Polytechnique, 2001-2007. | + | * **Former position**: |
| + | * Full time position: "Professeur chargé de cours", Ecole Polytechnique, 2001-2007. | ||
| + | * Part-time position: “Professeur chargé de cours”, Ecole Polytechnique, 2017-2023. | ||
| * **Area of Interest**: Computational Statistics, Applied Probability, Machine Learning. | * **Area of Interest**: Computational Statistics, Applied Probability, Machine Learning. | ||
| * **Keywords**: Hidden Markov Models, latent variable models, sequential Monte Carlo methods, Markov chains Monte Carlo, variational inference, statistical inference. | * **Keywords**: Hidden Markov Models, latent variable models, sequential Monte Carlo methods, Markov chains Monte Carlo, variational inference, statistical inference. | ||
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| If you click below, you can find a list of my published papers: | If you click below, you can find a list of my published papers: | ||
| - | <hidden ====Click here to see my published papers====> | + | <color /yellow> <hidden ====Click here to see my published papers====> </color> |
| <sortable r1> | <sortable r1> | ||
| <searchtable> | <searchtable> | ||
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| | 25 | R. Douc and E. Moulines | Asymptotic properties of the maximum likelihood estimation in misspecified Hidden Markov models | Annals of Statistics | 2012 | Oct. 2012, Volume 40, Number 5 (2012), 2697-2732. | | | | 25 | R. Douc and E. Moulines | Asymptotic properties of the maximum likelihood estimation in misspecified Hidden Markov models | Annals of Statistics | 2012 | Oct. 2012, Volume 40, Number 5 (2012), 2697-2732. | | | ||
| | 26 | R. Douc, P. Doukhan and E. Moulines | Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator | Stochastic Processes and their Applications | 2013 | Volume 123, Issue 7, July 2013, Pages 2620-2647 | | | | 26 | R. Douc, P. Doukhan and E. Moulines | Ergodicity of observation-driven time series models and consistency of the maximum likelihood estimator | Stochastic Processes and their Applications | 2013 | Volume 123, Issue 7, July 2013, Pages 2620-2647 | | | ||
| - | | 28 | M. Bédard, R. Douc and E. Moulines | Scaling analysis of Delayed Rejection MCMC methods | Methodology and Computing in Applied ProbabilitY | 2014 | Volume 16, Issue 4, P 811-838 | | | + | | 28 | M. Bédard, R. Douc and E. Moulines | Scaling analysis of Delayed Rejection MCMC methods | Methodology and Computing in Applied Probability | 2014 | Volume 16, Issue 4, P 811-838 | | |
| | 27 | C. Dubarry, R. Douc | Calibrating the exponential Ornstein-Uhlenbeck multiscale stochastic volatility model | Quantitative finance | 2013 | Volume 14, Issue 3, p 443-456 | | | | 27 | C. Dubarry, R. Douc | Calibrating the exponential Ornstein-Uhlenbeck multiscale stochastic volatility model | Quantitative finance | 2013 | Volume 14, Issue 3, p 443-456 | | | ||
| | 29 | R. Douc, E. Moulines and J. Olsson | Long-term stability of sequential Monte Carlo methods under verifiable conditions | Annals of Applied Probability | 2014 | Volume 24, No. 5, p 1767–1802 | | | | 29 | R. Douc, E. Moulines and J. Olsson | Long-term stability of sequential Monte Carlo methods under verifiable conditions | Annals of Applied Probability | 2014 | Volume 24, No. 5, p 1767–1802 | | | ||
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| | 41 | K. Daudel, R. Douc | Mixture weights optimisation for Alpha-Divergence Variational Inference | Advances in Neural Information Processing Systems, (Neurips) | 2021.\\ Nov. | 34 | | | | 41 | K. Daudel, R. Douc | Mixture weights optimisation for Alpha-Divergence Variational Inference | Advances in Neural Information Processing Systems, (Neurips) | 2021.\\ Nov. | 34 | | | ||
| | 42 | M. Gerber, R. Douc | A Global Stochastic Optimization Particle Filter Algorithm | Biometrika | 2022 | Volume 109, Issue 4, December 2022, Pages 937–955. | | | | 42 | M. Gerber, R. Douc | A Global Stochastic Optimization Particle Filter Algorithm | Biometrika | 2022 | Volume 109, Issue 4, December 2022, Pages 937–955. | | | ||
| - | | 43 | K. Daudel, R. Douc, F. Roueff | Monotonic alpha-divergence minimisation | Journal of Machine Learning Research (JMLR) | 2023 | (62):1−76, 2023. | | | + | | 43 | K. Daudel, R. Douc, F. Roueff | Monotonic alpha-divergence minimisation | Journal of Machine Learning Research (JMLR) | 2023 | (62):1−76, 2023. Vol 24. | | |
| | 44 | R. Douc, A. Durmus, A. Enfroy, J. Olsson | Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm | Submitted. [[https://arxiv.org/abs/2201.05002|Arxiv]] | 2023 | | | | | 44 | R. Douc, A. Durmus, A. Enfroy, J. Olsson | Boost your favorite Markov Chain Monte Carlo sampler using Kac's theorem: the Kick-Kac teleportation algorithm | Submitted. [[https://arxiv.org/abs/2201.05002|Arxiv]] | 2023 | | | | ||
| | 45 | R. Douc, P. Jacob, A. Lee, D. Vats | Solving the Poisson equation using coupled Markov chains | Submitted. [[https://arxiv.org/abs/2206.05691|Arxiv]] | 2023 | | | | | 45 | R. Douc, P. Jacob, A. Lee, D. Vats | Solving the Poisson equation using coupled Markov chains | Submitted. [[https://arxiv.org/abs/2206.05691|Arxiv]] | 2023 | | | | ||
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| | Stochastic calculus for finance | Majeure MSA, TSP | {{ :world:polycalculstofinance.pdf | Lecture Notes}} | Ito Calculus, stochastic integrals, stochastic differential equations, brownian motions | | | | Stochastic calculus for finance | Majeure MSA, TSP | {{ :world:polycalculstofinance.pdf | Lecture Notes}} | Ito Calculus, stochastic integrals, stochastic differential equations, brownian motions | | | ||
| | Monte Carlo with applications to finance | Majeure MSA, TSP | {{ :world:polymcfinanceenglish.pdf | Lecture Notes}} | Brownian bridges, monte carlo, variance reduction, Euler schemes | | | | Monte Carlo with applications to finance | Majeure MSA, TSP | {{ :world:polymcfinanceenglish.pdf | Lecture Notes}} | Brownian bridges, monte carlo, variance reduction, Euler schemes | | | ||
| - | | Markov Chain Monte Carlo: Theory and Practical applications | Master M2DS | {{ :world:poly.pdf | Lecture Notes}} | Mcmc methods, Metropolis Hastings, Pseudo marginal algorithms, Hamiltonian Monte Carlo | | | + | | Markov Chain Monte Carlo: Theory and Practical applications | Master M2DS | {{ :world:polymcmc.pdf | Lecture Notes}} | Mcmc methods, Metropolis Hastings, Pseudo marginal algorithms, Hamiltonian Monte Carlo | | |
| | Fundations of Machine Learning | 3rd year, Ecole Polytechnique | | | | | | Fundations of Machine Learning | 3rd year, Ecole Polytechnique | | | | | ||
| | Aléatoire | 1ère année, Ecole Polytechnique | | | | | | Aléatoire | 1ère année, Ecole Polytechnique | | | | | ||
world/homepage.txt · Last modified: 2023/10/13 18:23 by rdouc
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