Fredrik Lindsten

Orcid: 0000-0003-3749-5820

According to our database1, Fredrik Lindsten authored at least 59 papers between 2010 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
On the connection between Noise-Contrastive Estimation and Contrastive Divergence.
CoRR, 2024

2023
A Variational Perspective on Generative Flow Networks.
Trans. Mach. Learn. Res., 2023

Discriminator Guidance for Autoregressive Diffusion Models.
CoRR, 2023

Graph-based Neural Weather Prediction for Limited Area Modeling.
CoRR, 2023

Fast and scalable score-based kernel calibration tests.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks.
Proceedings of the Machine Learning, Optimization, and Data Science, 2023

Generalised Active Learning With Annotation Quality Selection.
Proceedings of the 33rd IEEE International Workshop on Machine Learning for Signal Processing, 2023

DINO as a von Mises-Fisher mixture model.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Enhancing Representation Learning with Deep Classifiers in Presence of Shortcut.
Proceedings of the IEEE International Conference on Acoustics, 2023

Temporal Graph Neural Networks for Irregular Data.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Active Learning with Weak Labels for Gaussian Processes.
CoRR, 2022

Active Learning with Weak Supervision for Gaussian Processes.
Proceedings of the Neural Information Processing - 29th International Conference, 2022

Scalable Deep Gaussian Markov Random Fields for General Graphs.
Proceedings of the International Conference on Machine Learning, 2022

Robustness and Reliability When Training With Noisy Labels.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021
Pseudo-Marginal Hamiltonian Monte Carlo.
J. Mach. Learn. Res., 2021

Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes.
Proceedings of the IEEE Intelligent Vehicles Symposium Workshops, 2021

Likelihood-free Out-of-Distribution Detection with Invertible Generative Models.
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Calibration tests beyond classification.
Proceedings of the 9th International Conference on Learning Representations, 2021

2020
Markovian Score Climbing: Variational Inference with KL(p||q).
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A General Framework for Ensemble Distribution Distillation.
Proceedings of the 30th IEEE International Workshop on Machine Learning for Signal Processing, 2020

Deep Gaussian Markov Random Fields.
Proceedings of the 37th International Conference on Machine Learning, 2020

Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood.
Proceedings of the 2020 IEEE International Conference on Acoustics, 2020

2019
High-Dimensional Filtering Using Nested Sequential Monte Carlo.
IEEE Trans. Signal Process., 2019

Elements of Sequential Monte Carlo.
Found. Trends Mach. Learn., 2019

Constructing the Matrix Multilayer Perceptron and its Application to the VAE.
CoRR, 2019

Bayesian nonparametric identification of Wiener systems.
Autom., 2019

Parameter elimination in particle Gibbs sampling.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Calibration tests in multi-class classification: A unifying framework.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Pseudo-Extended Markov chain Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian identification of state-space models via adaptive thermostats.
Proceedings of the 58th IEEE Conference on Decision and Control, 2019

Evaluating model calibration in classification.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Learning dynamical systems with particle stochastic approximation EM.
CoRR, 2018

Graphical model inference: Sequential Monte Carlo meets deterministic approximations.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Semi-Parametric Kernel-Based Identification of Wiener Systems.
Proceedings of the 57th IEEE Conference on Decision and Control, 2018

2017
Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations.
CoRR, 2017

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo.
CoRR, 2017

2016
Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models.
IEEE J. Sel. Top. Signal Process., 2016

Interacting Particle Markov Chain Monte Carlo.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems.
IEEE Trans. Signal Process., 2015

Particle Metropolis-Hastings using gradient and Hessian information.
Stat. Comput., 2015

Nested Sequential Monte Carlo Methods.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Particle Gibbs with refreshed backward simulation.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
Particle gibbs with ancestor sampling.
J. Mach. Learn. Res., 2014

Sequential Monte Carlo for Graphical Models.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Capacity estimation of two-dimensional channels using Sequential Monte Carlo.
Proceedings of the 2014 IEEE Information Theory Workshop, 2014

Identification of jump Markov linear models using particle filters.
Proceedings of the 53rd IEEE Conference on Decision and Control, 2014

2013
Backward Simulation Methods for Monte Carlo Statistical Inference.
Found. Trends Mach. Learn., 2013

Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM.
CoRR, 2013

Bayesian semiparametric Wiener system identification.
Autom., 2013

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Adaptive stopping for fast particle smoothing.
Proceedings of the IEEE International Conference on Acoustics, 2013

Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models.
Proceedings of the IEEE International Conference on Acoustics, 2013

An efficient stochastic approximation EM algorithm using conditional particle filters.
Proceedings of the IEEE International Conference on Acoustics, 2013

Particle metropolis hastings using Langevin dynamics.
Proceedings of the IEEE International Conference on Acoustics, 2013

2012
Ancestor Sampling for Particle Gibbs.
Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

On the use of backward simulation in the particle Gibbs sampler.
Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

2010
Geo-referencing for UAV navigation using environmental classification.
Proceedings of the IEEE International Conference on Robotics and Automation, 2010

Identification of mixed linear/nonlinear state-space models.
Proceedings of the 49th IEEE Conference on Decision and Control, 2010


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