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Picture for Fredrik Lindsten

Fredrik Lindsten

LIENS, INRIA Paris - Rocquencourt, MSR - INRIA

Robustness and reliability when training with noisy labels


Oct 07, 2021
Amanda Olmin, Fredrik Lindsten


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Markovian Score Climbing: Variational Inference with KL(p||q)


Mar 23, 2020
Christian A. Naesseth, Fredrik Lindsten, David Blei


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A general framework for ensemble distribution distillation


Feb 26, 2020
Jakob Lindqvist, Amanda Olmin, Fredrik Lindsten, Lennart Svensson


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Deep Gaussian Markov random fields


Feb 18, 2020
Per Sidén, Fredrik Lindsten


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Parameter elimination in particle Gibbs sampling


Oct 30, 2019
Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten


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Calibration tests in multi-class classification: A unifying framework


Oct 24, 2019
David Widmann, Fredrik Lindsten, Dave Zachariah


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Particle filter with rejection control and unbiased estimator of the marginal likelihood


Oct 21, 2019
Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön, Fredrik Lindsten


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Elements of Sequential Monte Carlo


Mar 12, 2019
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

* Under review at Foundations and Trends in Machine Learning 

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Evaluating model calibration in classification


Feb 19, 2019
Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön


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Constructing the Matrix Multilayer Perceptron and its Application to the VAE


Feb 04, 2019
Jalil Taghia, Maria BÄnkestad, Fredrik Lindsten, Thomas B. Schön


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Graphical model inference: Sequential Monte Carlo meets deterministic approximations


Jan 08, 2019
Fredrik Lindsten, Jouni Helske, Matti Vihola

* 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montr\'eal, Canada 

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Learning dynamical systems with particle stochastic approximation EM


Jun 25, 2018
Andreas Svensson, Fredrik Lindsten


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Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo


Dec 15, 2017
Thomas B. Schön, Andreas Svensson, Lawrence Murray, Fredrik Lindsten

* Thomas B. Sch\"on, Andreas Svensson, Lawrence Murray and Fredrik Lindsten, 2018. Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. In Mechanical Systems and Signal Processing, Volume 104, pp. 866-883 

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Learning of state-space models with highly informative observations: a tempered Sequential Monte Carlo solution


Dec 13, 2017
Andreas Svensson, Thomas B. Schön, Fredrik Lindsten

* Mechanical Systems and Signal Processing, Volume 104 (May 2018), Pages 915-928 

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Pseudo-extended Markov chain Monte Carlo


Aug 17, 2017
Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman


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Interacting Particle Markov Chain Monte Carlo


Apr 12, 2017
Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank Wood

* JMLR W&CP 48 : 2616-2625, 2016 

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High-dimensional Filtering using Nested Sequential Monte Carlo


Dec 29, 2016
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön


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Pseudo-Marginal Hamiltonian Monte Carlo


Jul 08, 2016
Fredrik Lindsten, Arnaud Doucet


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Sequential Monte Carlo Methods for System Identification


Mar 10, 2016
Thomas B. Schön, Fredrik Lindsten, Johan Dahlin, Johan WÄgberg, Christian A. Naesseth, Andreas Svensson, Liang Dai

* In proceedings of the 17th IFAC Symposium on System Identification (SYSID). Added cover page 

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Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables


Nov 17, 2015
Johan Dahlin, Fredrik Lindsten, Joel Kronander, Thomas B. Schön

* 23 pages, 5 figures 

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Nested Sequential Monte Carlo Methods


Sep 11, 2015
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön

* Extended version of paper published in Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, 2015 

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Quasi-Newton particle Metropolis-Hastings


Sep 02, 2015
Johan Dahlin, Fredrik Lindsten, Thomas B. Schön

* 23 pages, 5 figures. Accepted for the 17th IFAC Symposium on System Identification (SYSID), Beijing, China, October 2015 

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Divide-and-Conquer with Sequential Monte Carlo


Jun 30, 2015
Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth, Bonnie Kirkpatrick, Thomas B. Schön, John Aston, Alexandre Bouchard-CÎté

* Journal of Computational and Graphical Statistics, 26(2):445-458, 2017 

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Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering


Feb 10, 2015
Simon Lacoste-Julien, Fredrik Lindsten, Francis Bach

* in 18th International Conference on Artificial Intelligence and Statistics (AISTATS), May 2015, San Diego, United States. 38, JMLR Workshop and Conference Proceedings 

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Sequential Monte Carlo for Graphical Models


Oct 06, 2014
Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön


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Identification of jump Markov linear models using particle filters


Sep 25, 2014
Andreas Svensson, Thomas B. Schön, Fredrik Lindsten

* Proc. of IEEE 53rd Conference on Decision and Control (CDC), pp.6504,6509, 15-17 Dec. 2014 (Los Angeles, CA, USA) 
* Accepted to 53rd IEEE International Conference on Decision and Control (CDC), 2014 (Los Angeles, CA, USA) 

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Particle Metropolis-Hastings using gradient and Hessian information


Sep 18, 2014
Johan Dahlin, Fredrik Lindsten, Thomas B. Schön

* Statistics and Computing, Volume 25, Issue 1, pp 81-92, 2015 
* 27 pages, 5 figures, 2 tables. The final publication is available at Springer via: http://dx.doi.org/10.1007/s11222-014-9510-0 

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Particle filter-based Gaussian process optimisation for parameter inference


Mar 31, 2014
Johan Dahlin, Fredrik Lindsten

* Accepted for publication in proceedings of the 19th World Congress of the International Federation of Automatic Control (IFAC), Cape Town, South Africa, August 2014. 6 pages, 4 figures 

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Particle Gibbs with Ancestor Sampling


Jan 03, 2014
Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön

* Journal of Machine Learning Research, 15 (2014) 2145-2184 

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Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM


Dec 17, 2013
Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen


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