Fredrik Lindsten

106 papers A* 18A 10B 1Misc 8Journal 56Unranked 13

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Particle-Guided Diffusion Models for Partial Differential Equations. CoRR Andrew Millard, Fredrik Lindsten, Zheng Zhao
2025	J	`jnl`	Building Machine Learning Limited Area Models: Kilometer-Scale Weather Forecasting in Realistic Settings. CoRR Simon Adamov, Joel Oskarsson, Leif C. Denby, Tomas Landelius, Kasper Hintz, Simon Christiansen, Irene Schicker, Carlos Osuna, Fredrik Lindsten, Oliver Fuhrer, Sebastian Schemm
2025	J	`jnl`	CRPS-LAM: Regional ensemble weather forecasting from matching marginals. CoRR Erik Larsson, Joel Oskarsson, Tomas Landelius, Fredrik Lindsten
2025	A*	`conf`	Continuous Ensemble Weather Forecasting with Diffusion models. ICLR Martin Andrae, Tomas Landelius, Joel Oskarsson, Fredrik Lindsten
2025	J	`jnl`	DAISI: Data Assimilation with Inverse Sampling using Stochastic Interpolants. CoRR Martin Andrae, Erik Larsson, So Takao, Tomas Landelius, Fredrik Lindsten
2025	J	`jnl`	Diffusion-LAM: Probabilistic Limited Area Weather Forecasting with Diffusion. CoRR Erik Larsson, Joel Oskarsson, Tomas Landelius, Fredrik Lindsten
2025	J	`jnl`	Discriminative Ordering Through Ensemble Consensus. CoRR Louis Ohl, Fredrik Lindsten
2025	A	`conf`	Discriminative ordering through ensemble consensus. UAI Louis Ohl, Fredrik Lindsten
2025	J	`jnl`	ESS-Flow: Training-free guidance of flow-based models as inference in source space. CoRR Adhithyan Kalaivanan, Zheng Zhao, Jens Sjölund, Fredrik Lindsten
2025	J	`jnl`	Fast and Scalable Score-Based Kernel Calibration Tests. CoRR Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton
2025	J	`jnl`	Improved Contrastive Predictive Coding for Time Series Out-Of-Distribution Detection Applied to Human Activity Data. Pattern Recognit. Lett. Amirhossein Ahmadian, Fredrik Lindsten
2025	J	`jnl`	Prior Learning in Introspective VAEs. Trans. Mach. Learn. Res. Ioannis Athanasiadis, Fredrik Lindsten, Michael Felsberg
2025	—	`conf`	Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations. SCIA (2) Yifan Ding, Arturas Aleksandrauskas, Amirhossein Ahmadian, Jonas Unger, Fredrik Lindsten, Gabriel Eilertsen
2025	J	`jnl`	Revisiting Likelihood-Based Out-of-Distribution Detection by Modeling Representations. CoRR Yifan Ding, Arturas Aleksandrauskas, Amirhossein Ahmadian, Jonas Unger, Fredrik Lindsten, Gabriel Eilertsen
2025	A*	`conf`	Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo. ICML Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten
2025	J	`jnl`	Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo. CoRR Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten
2025	J	`jnl`	Understanding Ice Crystal Habit Diversity with Self-Supervised Learning. CoRR Joseph Ko, Hariprasath Govindarajan, Fredrik Lindsten, Vanessa Przybylo, Kara Sulia, Marcus van Lier-Walqui, Kara D. Lamb
2025	A*	`conf`	WyckoffDiff - A Generative Diffusion Model for Crystal Symmetry. ICML Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal, Dong Qian, Rickard Armiento, Fredrik Lindsten
2025	J	`jnl`	WyckoffDiff - A Generative Diffusion Model for Crystal Symmetry. CoRR Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S. Parackal, Dong Qian, Rickard Armiento, Fredrik Lindsten
2025	A*	`conf`	cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM. ICLR Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff, Fredrik Lindsten
2024	J	`jnl`	Continuous Ensemble Weather Forecasting with Diffusion models. CoRR Martin Andrae, Tomas Landelius, Joel Oskarsson, Fredrik Lindsten
2024	J	`jnl`	DINO as a von Mises-Fisher mixture model. CoRR Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
2024	A	`conf`	Discriminator Guidance for Autoregressive Diffusion Models. AISTATS Filip Ekström Kelvinius, Fredrik Lindsten
2024	A	`conf`	On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods. BMVC Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
2024	J	`jnl`	On Partial Prototype Collapse in the DINO Family of Self-Supervised Methods. CoRR Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
2024	A	`conf`	On the connection between Noise-Contrastive Estimation and Contrastive Divergence. AISTATS Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten
2024	J	`jnl`	On the connection between Noise-Contrastive Estimation and Contrastive Divergence. CoRR Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten
2024	J	`jnl`	Prior Learning in Introspective VAEs. CoRR Ioannis Athanasiadis, Shashi Nagarajan, Fredrik Lindsten, Michael Felsberg
2024	A*	`conf`	Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. NeurIPS Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten
2024	J	`jnl`	Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks. CoRR Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten
2024	J	`jnl`	Towards understanding epoch-wise double descent in two-layer linear neural networks. CoRR Amanda Olmin, Fredrik Lindsten
2024	A	`conf`	Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio. AISTATS Amirhossein Ahmadian, Yifan Ding, Gabriel Eilertsen, Fredrik Lindsten
2024	J	`jnl`	cryoSPHERE: Single-particle heterogeneous reconstruction from cryo EM. CoRR Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff, Fredrik Lindsten
2023	J	`jnl`	A Variational Perspective on Generative Flow Networks. Trans. Mach. Learn. Res. Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth
2023	A*	`conf`	DINO as a von Mises-Fisher mixture model. ICLR Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
2023	J	`jnl`	Discriminator Guidance for Autoregressive Diffusion Models. CoRR Filip Ekström Kelvinius, Fredrik Lindsten
2023	Misc	`conf`	Enhancing Representation Learning with Deep Classifiers in Presence of Shortcut. ICASSP Amirhossein Ahmadian, Fredrik Lindsten
2023	A	`conf`	Fast and scalable score-based kernel calibration tests. UAI Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton
2023	—	`conf`	Generalised Active Learning With Annotation Quality Selection. MLSP Jakob Lindqvist, Amanda Olmin, Lennart Svensson, Fredrik Lindsten
2023	J	`jnl`	Graph-based Neural Weather Prediction for Limited Area Modeling. CoRR Joel Oskarsson, Tomas Landelius, Fredrik Lindsten
2023	—	`conf`	Speeding Up Logic-Based Benders Decomposition by Strengthening Cuts with Graph Neural Networks. LOD (1) Johannes Varga, Emil Karlsson, Günther R. Raidl, Elina Rönnberg, Fredrik Lindsten, Tobias Rodemann
2023	A	`conf`	Temporal Graph Neural Networks for Irregular Data. AISTATS Joel Oskarsson, Per Sidén, Fredrik Lindsten
2023	J	`jnl`	Temporal Graph Neural Networks for Irregular Data. CoRR Joel Oskarsson, Per Sidén, Fredrik Lindsten
2022	J	`jnl`	A Variational Perspective on Generative Flow Networks. CoRR Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A. Naesseth
2022	J	`jnl`	Active Learning with Weak Labels for Gaussian Processes. CoRR Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten
2022	—	`conf`	Active Learning with Weak Supervision for Gaussian Processes. ICONIP (5) Amanda Olmin, Jakob Lindqvist, Lennart Svensson, Fredrik Lindsten
2022	J	`jnl`	Calibration tests beyond classification. CoRR David Widmann, Fredrik Lindsten, Dave Zachariah
2022	A	`conf`	Robustness and Reliability When Training With Noisy Labels. AISTATS Amanda Olmin, Fredrik Lindsten
2022	A*	`conf`	Scalable Deep Gaussian Markov Random Fields for General Graphs. ICML Joel Oskarsson, Per Sidén, Fredrik Lindsten
2022	J	`jnl`	Scalable Deep Gaussian Markov Random Fields for General Graphs. CoRR Joel Oskarsson, Per Sidén, Fredrik Lindsten
2021	A*	`conf`	Calibration tests beyond classification. ICLR David Widmann, Fredrik Lindsten, Dave Zachariah
2021	A*	`conf`	Likelihood-free Out-of-Distribution Detection with Invertible Generative Models. IJCAI Amirhossein Ahmadian, Fredrik Lindsten
2021	J	`jnl`	Pseudo-Marginal Hamiltonian Monte Carlo. J. Mach. Learn. Res. Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten
2021	J	`jnl`	Robustness and reliability when training with noisy labels. CoRR Amanda Olmin, Fredrik Lindsten
2021	—	`conf`	Self-Supervised Representation Learning for Content Based Image Retrieval of Complex Scenes. IV Workshops Hariprasath Govindarajan, Peter Lindskog, Dennis Lundström, Amanda Olmin, Jacob Roll, Fredrik Lindsten
2020	—	`conf`	A General Framework for Ensemble Distribution Distillation. MLSP Jakob Lindqvist, Amanda Olmin, Fredrik Lindsten, Lennart Svensson
2020	J	`jnl`	A general framework for ensemble distribution distillation. CoRR Jakob Lindqvist, Amanda Olmin, Fredrik Lindsten, Lennart Svensson
2020	A*	`conf`	Deep Gaussian Markov Random Fields. ICML Per Sidén, Fredrik Lindsten
2020	J	`jnl`	Deep Gaussian Markov random fields. CoRR Per Sidén, Fredrik Lindsten
2020	A*	`conf`	Markovian Score Climbing: Variational Inference with KL(p\|\|q). NeurIPS Christian A. Naesseth, Fredrik Lindsten, David M. Blei
2020	J	`jnl`	Markovian Score Climbing: Variational Inference with KL(p\|\|q). CoRR Christian A. Naesseth, Fredrik Lindsten, David M. Blei
2020	Misc	`conf`	Particle Filter with Rejection Control and Unbiased Estimator of the Marginal Likelihood. ICASSP Jan Kudlicka, Lawrence M. Murray, Thomas B. Schön, Fredrik Lindsten
2019	—	`conf`	Bayesian identification of state-space models via adaptive thermostats. CDC Jack Umenberger, Thomas B. Schön, Fredrik Lindsten
2019	J	`jnl`	Bayesian nonparametric identification of Wiener systems. Autom. Riccardo Sven Risuleo, Fredrik Lindsten, Håkan Hjalmarsson
2019	A*	`conf`	Calibration tests in multi-class classification: A unifying framework. NeurIPS David Widmann, Fredrik Lindsten, Dave Zachariah
2019	J	`jnl`	Calibration tests in multi-class classification: A unifying framework. CoRR David Widmann, Fredrik Lindsten, Dave Zachariah
2019	J	`jnl`	Constructing the Matrix Multilayer Perceptron and its Application to the VAE. CoRR Jalil Taghia, Maria Bånkestad, Fredrik Lindsten, Thomas B. Schön
2019	J	`jnl`	Elements of Sequential Monte Carlo. CoRR Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
2019	J	`jnl`	Elements of Sequential Monte Carlo. Found. Trends Mach. Learn. Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
2019	A	`conf`	Evaluating model calibration in classification. AISTATS Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019	J	`jnl`	Evaluating model calibration in classification. CoRR Juozas Vaicenavicius, David Widmann, Carl R. Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019	J	`jnl`	Graphical model inference: Sequential Monte Carlo meets deterministic approximations. CoRR Fredrik Lindsten, Jouni Helske, Matti Vihola
2019	J	`jnl`	High-Dimensional Filtering Using Nested Sequential Monte Carlo. IEEE Trans. Signal Process. Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
2019	A*	`conf`	Parameter elimination in particle Gibbs sampling. NeurIPS Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten
2019	A*	`conf`	Pseudo-Extended Markov chain Monte Carlo. NeurIPS Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman
2018	A*	`conf`	Graphical model inference: Sequential Monte Carlo meets deterministic approximations. NeurIPS Fredrik Lindsten, Jouni Helske, Matti Vihola
2018	J	`jnl`	Learning dynamical systems with particle stochastic approximation EM. CoRR Andreas Svensson, Fredrik Lindsten
2018	—	`conf`	Semi-Parametric Kernel-Based Identification of Wiener Systems. CDC Riccardo Sven Risuleo, Fredrik Lindsten, Håkan Hjalmarsson
2017	J	`jnl`	Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations. CoRR Andreas Svensson, Fredrik Lindsten, Thomas B. Schön
2017	J	`jnl`	Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo. CoRR Thomas B. Schön, Andreas Svensson, Lawrence M. Murray, Fredrik Lindsten
2016	A*	`conf`	Interacting Particle Markov Chain Monte Carlo. ICML Tom Rainforth, Christian A. Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem van de Meent, Arnaud Doucet, Frank D. Wood
2016	J	`jnl`	Rao-Blackwellized Particle Smoothers for Conditionally Linear Gaussian Models. IEEE J. Sel. Top. Signal Process. Fredrik Lindsten, Pete Bunch, Simo Särkkä, Thomas B. Schön, Simon J. Godsill
2015	A*	`conf`	Nested Sequential Monte Carlo Methods. ICML Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
2015	Misc	`conf`	Particle Gibbs with refreshed backward simulation. ICASSP Pete Bunch, Fredrik Lindsten, Sumeetpal S. Singh
2015	J	`jnl`	Particle Metropolis-Hastings using gradient and Hessian information. Stat. Comput. Johan Dahlin, Fredrik Lindsten, Thomas B. Schön
2015	J	`jnl`	Recursive Maximum Likelihood Identification of Jump Markov Nonlinear Systems. IEEE Trans. Signal Process. Emre Özkan, Fredrik Lindsten, Carsten Fritsche, Fredrik Gustafsson
2015	A	`conf`	Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. AISTATS Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach
2015	J	`jnl`	Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering. CoRR Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach
2014	B	`conf`	Capacity estimation of two-dimensional channels using Sequential Monte Carlo. ITW Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
2014	J	`jnl`	Capacity estimation of two-dimensional channels using Sequential Monte Carlo. CoRR Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
2014	—	`conf`	Identification of jump Markov linear models using particle filters. CDC Andreas Svensson, Thomas B. Schön, Fredrik Lindsten
2014	J	`jnl`	Particle gibbs with ancestor sampling. J. Mach. Learn. Res. Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
2014	—	`conf`	Sequential Monte Carlo for Graphical Models. NIPS Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
2013	Misc	`conf`	Adaptive stopping for fast particle smoothing. ICASSP Ehsan Taghavi, Fredrik Lindsten, Lennart Svensson, Thomas B. Schön
2013	Misc	`conf`	An efficient stochastic approximation EM algorithm using conditional particle filters. ICASSP Fredrik Lindsten
2013	J	`jnl`	Backward Simulation Methods for Monte Carlo Statistical Inference. Found. Trends Mach. Learn. Fredrik Lindsten, Thomas B. Schön
2013	—	`conf`	Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. NIPS Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen
2013	J	`jnl`	Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC. CoRR Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen
2013	J	`jnl`	Bayesian semiparametric Wiener system identification. Autom. Fredrik Lindsten, Thomas B. Schön, Michael I. Jordan
2013	J	`jnl`	Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM. CoRR Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen
2013	Misc	`conf`	Particle metropolis hastings using Langevin dynamics. ICASSP Johan Dahlin, Fredrik Lindsten, Thomas B. Schön
2013	Misc	`conf`	Rao-Blackwellized particle smoothers for mixed linear/nonlinear state-space models. ICASSP Fredrik Lindsten, Pete Bunch, Simon J. Godsill, Thomas B. Schön
2012	—	`conf`	Ancestor Sampling for Particle Gibbs. NIPS Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
2012	Misc	`conf`	On the use of backward simulation in the particle Gibbs sampler. ICASSP Fredrik Lindsten, Thomas B. Schön
2010	A*	`conf`	Geo-referencing for UAV navigation using environmental classification. ICRA Fredrik Lindsten, Jonas Callmer, Henrik Ohlsson, David Törnqvist, Thomas B. Schön, Fredrik Gustafsson
2010	—	`conf`	Identification of mixed linear/nonlinear state-space models. CDC Fredrik Lindsten, Thomas B. Schön