Identification of mixed linear/nonlinear state-space models. - Fredrik Lindsten; Thomas B. Schön (CDC, 2010)

Geo-referencing for UAV navigation using environmental classification. - Fredrik Lindsten; Jonas Callmer; Henrik Ohlsson; David Törnqvist; Thomas B. Schön; Fredrik Gustafsson (ICRA, 2010)

On the use of backward simulation in the particle Gibbs sampler. - Fredrik Lindsten; Thomas B. Schön (ICASSP, 2012)

Ancestor Sampling for Particle Gibbs. - Fredrik Lindsten; Michael I. Jordan; Thomas B. Schön (NIPS, 2012)

Particle metropolis hastings using Langevin dynamics. - Johan Dahlin; Fredrik Lindsten; Thomas B. Schön (ICASSP, 2013)

An efficient stochastic approximation EM algorithm using conditional particle filters. - Fredrik Lindsten (ICASSP, 2013)

Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models. - Fredrik Lindsten; Pete Bunch; Simon J. Godsill; Thomas B. Schön (ICASSP, 2013)

Adaptive stopping for fast particle smoothing. - Ehsan Taghavi; Fredrik Lindsten; Lennart Svensson; Thomas B. Schön (ICASSP, 2013)

Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. - Roger Frigola; Fredrik Lindsten; Thomas B. Schön; Carl E. Rasmussen (NIPS, 2013)

Bayesian semiparametric Wiener system identification. - Fredrik Lindsten; Thomas B. Schön; Michael I. Jordan (Autom., 2013)

Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM. - Roger Frigola; Fredrik Lindsten; Thomas B. Schön; Carl E. Rasmussen (CoRR, 2013)

Backward Simulation Methods for Monte Carlo Statistical Inference. - Fredrik Lindsten; Thomas B. Schön (Found. Trends Mach. Learn., 2013)

Identification of jump Markov linear models using particle filters. - Andreas Svensson; Thomas B. Schön; Fredrik Lindsten (CDC, 2014)

Capacity estimation of two-dimensional channels using Sequential Monte Carlo. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (ITW, 2014)

Sequential Monte Carlo for Graphical Models. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (NIPS, 2014)

Particle gibbs with ancestor sampling. - Fredrik Lindsten; Michael I. Jordan; Thomas B. Schön (J. Mach. Learn. Res., 2014)

Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. - Simon Lacoste-Julien; Fredrik Lindsten; Francis R. Bach (AISTATS, 2015)

Particle Gibbs with refreshed backward simulation. - Pete Bunch; Fredrik Lindsten; Sumeetpal S. Singh (ICASSP, 2015)

Nested Sequential Monte Carlo Methods. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (ICML, 2015)

Particle Metropolis-Hastings using gradient and Hessian information. - Johan Dahlin; Fredrik Lindsten; Thomas B. Schön (Stat. Comput., 2015)

Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems. - Emre Özkan; Fredrik Lindsten; Carsten Fritsche; Fredrik Gustafsson (IEEE Trans. Signal Process., 2015)

Interacting Particle Markov Chain Monte Carlo. - Tom Rainforth; Christian A. Naesseth; Fredrik Lindsten; Brooks Paige; Jan-Willem van de Meent; Arnaud Doucet; Frank D. Wood (ICML, 2016)

Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models. - Fredrik Lindsten; Pete Bunch; Simo Särkkä; Thomas B. Schön; Simon J. Godsill (IEEE J. Sel. Top. Signal Process., 2016)

Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. - Thomas B. Schön; Andreas Svensson; Lawrence M. Murray; Fredrik Lindsten (CoRR, 2017)

Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. - Andreas Svensson; Fredrik Lindsten; Thomas B. Schön (CoRR, 2017)

Semi-Parametric Kernel-Based Identification of Wiener Systems. - Riccardo Sven Risuleo; Fredrik Lindsten; Håkan Hjalmarsson (CDC, 2018)

Graphical model inference: Sequential Monte Carlo meets deterministic approximations. - Fredrik Lindsten; Jouni Helske; Matti Vihola (NeurIPS, 2018)

Learning dynamical systems with particle stochastic approximation EM. - Andreas Svensson; Fredrik Lindsten (CoRR, 2018)

Evaluating model calibration in classification. - Juozas Vaicenavicius; David Widmann; Carl R. Andersson; Fredrik Lindsten; Jacob Roll; Thomas B. Schön (AISTATS, 2019)

Bayesian identification of state-space models via adaptive thermostats. - Jack Umenberger; Thomas B. Schön; Fredrik Lindsten (CDC, 2019)

Pseudo-Extended Markov chain Monte Carlo. - Christopher Nemeth; Fredrik Lindsten; Maurizio Filippone; James Hensman (NeurIPS, 2019)

Calibration tests in multi-class classification: A unifying framework. - David Widmann; Fredrik Lindsten; Dave Zachariah (NeurIPS, 2019)

Parameter elimination in particle Gibbs sampling. - Anna Wigren; Riccardo Sven Risuleo; Lawrence Murray; Fredrik Lindsten (NeurIPS, 2019)

Bayesian nonparametric identification of Wiener systems. - Riccardo Sven Risuleo; Fredrik Lindsten; Håkan Hjalmarsson (Autom., 2019)

Constructing the Matrix Multilayer Perceptron and its Application to the VAE. - Jalil Taghia; Maria Bånkestad; Fredrik Lindsten; Thomas B. Schön (CoRR, 2019)

Elements of Sequential Monte Carlo. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (Found. Trends Mach. Learn., 2019)

High-Dimensional Filtering Using Nested Sequential Monte Carlo. - Christian A. Naesseth; Fredrik Lindsten; Thomas B. Schön (IEEE Trans. Signal Process., 2019)

Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood. - Jan Kudlicka; Lawrence M. Murray; Thomas B. Schön; Fredrik Lindsten (ICASSP, 2020)

Deep Gaussian Markov Random Fields. - Per Sidén; Fredrik Lindsten (ICML, 2020)

A General Framework for Ensemble Distribution Distillation. - Jakob Lindqvist; Amanda Olmin; Fredrik Lindsten; Lennart Svensson (MLSP, 2020)

Markovian Score Climbing: Variational Inference with KL(p||q). - Christian A. Naesseth; Fredrik Lindsten; David M. Blei (NeurIPS, 2020)

Calibration tests beyond classification. - David Widmann; Fredrik Lindsten; Dave Zachariah (ICLR, 2021)

Likelihood-free Out-of-Distribution Detection with Invertible Generative Models. - Amirhossein Ahmadian; Fredrik Lindsten (IJCAI, 2021)

Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes. - Hariprasath Govindarajan; Peter Lindskog; Dennis Lundström; Amanda Olmin; Jacob Roll; Fredrik Lindsten (IV Workshops, 2021)

Pseudo-Marginal Hamiltonian Monte Carlo. - Johan Alenlöv; Arnoud Doucet; Fredrik Lindsten (J. Mach. Learn. Res., 2021)

Robustness and Reliability When Training With Noisy Labels. - Amanda Olmin; Fredrik Lindsten (AISTATS, 2022)

Scalable Deep Gaussian Markov Random Fields for General Graphs. - Joel Oskarsson; Per Sidén; Fredrik Lindsten (ICML, 2022)

Active Learning with Weak Supervision for Gaussian Processes. - Amanda Olmin; Jakob Lindqvist; Lennart Svensson; Fredrik Lindsten (ICONIP (5), 2022)

Active Learning with Weak Labels for Gaussian Processes. - Amanda Olmin; Jakob Lindqvist; Lennart Svensson; Fredrik Lindsten (CoRR, 2022)

Temporal Graph Neural Networks for Irregular Data. - Joel Oskarsson; Per Sidén; Fredrik Lindsten (AISTATS, 2023)

Enhancing Representation Learning with Deep Classifiers in Presence of Shortcut. - Amirhossein Ahmadian; Fredrik Lindsten (ICASSP, 2023)

DINO as a von Mises-Fisher mixture model. - Hariprasath Govindarajan; Per Sidén; Jacob Roll; Fredrik Lindsten (ICLR, 2023)

Generalised Active Learning With Annotation Quality Selection. - Jakob Lindqvist; Amanda Olmin; Lennart Svensson; Fredrik Lindsten (MLSP, 2023)

Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks. - Johannes Varga; Emil Karlsson; Günther R. Raidl; Elina Rönnberg; Fredrik Lindsten; Tobias Rodemann (LOD (1), 2023)

Fast and scalable score-based kernel calibration tests. - Pierre Glaser; David Widmann; Fredrik Lindsten; Arthur Gretton (UAI, 2023)

Graph-based Neural Weather Prediction for Limited Area Modeling. - Joel Oskarsson; Tomas Landelius; Fredrik Lindsten (CoRR, 2023)

A Variational Perspective on Generative Flow Networks. - Heiko Zimmermann; Fredrik Lindsten; Jan-Willem van de Meent; Christian A. Naesseth (Trans. Mach. Learn. Res., 2023)

Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio. - Amirhossein Ahmadian; Yifan Ding; Gabriel Eilertsen; Fredrik Lindsten (AISTATS, 2024)

Discriminator Guidance for Autoregressive Diffusion Models. - Filip Ekström Kelvinius; Fredrik Lindsten (AISTATS, 2024)

On the connection between Noise-Contrastive Estimation and Contrastive Divergence. - Amanda Olmin; Jakob Lindqvist; Lennart Svensson; Fredrik Lindsten (AISTATS, 2024)

On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods. - Hariprasath Govindarajan; Per Sidén; Jacob Roll; Fredrik Lindsten (BMVC, 2024)

Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. - Joel Oskarsson; Tomas Landelius; Marc Peter Deisenroth; Fredrik Lindsten (NeurIPS, 2024)

Towards understanding epoch-wise double descent in two-layer linear neural networks. - Amanda Olmin; Fredrik Lindsten (CoRR, 2024)

Prior Learning in Introspective VAEs. - Ioannis Athanasiadis; Shashi Nagarajan; Fredrik Lindsten; Michael Felsberg (CoRR, 2024)

Continuous Ensemble Weather Forecasting with Diffusion models. - Martin Andrae; Tomas Landelius; Joel Oskarsson; Fredrik Lindsten (ICLR, 2025)

cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM. - Gabriel Ducrocq; Lukas Grunewald; Sebastian Westenhoff; Fredrik Lindsten (ICLR, 2025)

Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations. - Yifan Ding; Arturas Aleksandrauskas; Amirhossein Ahmadian; Jonas Unger; Fredrik Lindsten; Gabriel Eilertsen (SCIA (2), 2025)

Discriminative ordering through ensemble consensus. - Louis Ohl; Fredrik Lindsten (UAI, 2025)

Improved Contrastive Predictive Coding for Time Series Out-Of-Distribution Detection Applied to Human Activity Data. - Amirhossein Ahmadian; Fredrik Lindsten (Pattern Recognit. Lett., 2025)

Prior Learning in Introspective VAEs. - Ioannis Athanasiadis; Fredrik Lindsten; Michael Felsberg (Trans. Mach. Learn. Res., 2025)

Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo. - Filip Ekström Kelvinius; Zheng Zhao; Fredrik Lindsten (CoRR, 2025)

WyckoffDiff - A Generative Diffusion Model for Crystal Symmetry. - Filip Ekström Kelvinius; Oskar B. Andersson; Abhijith S. Parackal; Dong Qian; Rickard Armiento; Fredrik Lindsten (CoRR, 2025)

Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion. - Erik Larsson; Joel Oskarsson; Tomas Landelius; Fredrik Lindsten (CoRR, 2025)

Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings. - Simon Adamov; Joel Oskarsson; Leif Denby; Tomas Landelius; Kasper Hintz; Simon Christiansen; Irene Schicker; Carlos Osuna; Fredrik Lindsten; Oliver Fuhrer; Sebastian Schemm (CoRR, 2025)