ML Anthology

Lindsten, Fredrik

36 publications

ICLR 2025 Continuous Ensemble Weather Forecasting with Diffusion Models Martin Andrae, Tomas Landelius, Joel Oskarsson, Fredrik Lindsten
UAI 2025 Discriminative Ordering Through Ensemble Consensus Louis Ohl, Fredrik Lindsten
TMLR 2025 Prior Learning in Introspective VAEs Ioannis Athanasiadis, Fredrik Lindsten, Michael Felsberg
ICML 2025 Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo Filip Ekström Kelvinius, Zheng Zhao, Fredrik Lindsten
ICML 2025 WyckoffDiff – A Generative Diffusion Model for Crystal Symmetry Filip Ekström Kelvinius, Oskar B. Andersson, Abhijith S Parackal, Dong Qian, Rickard Armiento, Fredrik Lindsten
ICLR 2025 cryoSPHERE: Single-Particle HEterogeneous REconstruction from Cryo EM Gabriel Ducrocq, Lukas Grunewald, Sebastian Westenhoff, 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
NeurIPS 2024 Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks Joel Oskarsson, Tomas Landelius, Marc Peter Deisenroth, Fredrik Lindsten
AISTATS 2024 Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio Amirhossein Ahmadian, Yifan Ding, Gabriel Eilertsen, Fredrik Lindsten
TMLR 2023 A Variational Perspective on Generative Flow Networks Heiko Zimmermann, Fredrik Lindsten, Jan-Willem van de Meent, Christian A Naesseth
ICMLW 2023 Autoregressive Diffusion Models with Non-Uniform Generation Order Filip Ekström Kelvinius, Fredrik Lindsten
ICLR 2023 DINO as a Von Mises-Fisher Mixture Model Hariprasath Govindarajan, Per Sidén, Jacob Roll, Fredrik Lindsten
UAI 2023 Fast and Scalable Score-Based Kernel Calibration Tests Pierre Glaser, David Widmann, Fredrik Lindsten, Arthur Gretton
AISTATS 2023 Temporal Graph Neural Networks for Irregular Data Joel Oskarsson, Per Sidén, Fredrik Lindsten
AISTATS 2022 Robustness and Reliability When Training with Noisy Labels Amanda Olmin, Fredrik Lindsten
NeurIPSW 2022 A Modelling Framework for Catalysing Progress in the Rod-Shaped Bacterial Cell Growth Discourse Shashi Nagarajan, Fredrik Lindsten
ICML 2022 Scalable Deep Gaussian Markov Random Fields for General Graphs Joel Oskarsson, Per Sidén, Fredrik Lindsten
ICLR 2021 Calibration Tests Beyond Classification David Widmann, Fredrik Lindsten, Dave Zachariah
IJCAI 2021 Likelihood-Free Out-of-Distribution Detection with Invertible Generative Models Amirhossein Ahmadian, Fredrik Lindsten
JMLR 2021 Pseudo-Marginal Hamiltonian Monte Carlo Johan Alenlöv, Arnoud Doucet, Fredrik Lindsten
NeurIPS 2020 Markovian Score Climbing: Variational Inference with KL(p||q) Christian Naesseth, Fredrik Lindsten, David M. Blei
NeurIPS 2019 Calibration Tests in Multi-Class Classification: A Unifying Framework David Widmann, Fredrik Lindsten, Dave Zachariah
FnTML 2019 Elements of Sequential Monte Carlo Christian A. Naesseth, Fredrik Lindsten, Thomas B. Schön
AISTATS 2019 Evaluating Model Calibration in Classification Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas Schön
NeurIPS 2019 Parameter Elimination in Particle Gibbs Sampling Anna Wigren, Riccardo Sven Risuleo, Lawrence Murray, Fredrik Lindsten
NeurIPS 2019 Pseudo-Extended Markov Chain Monte Carlo Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman
NeurIPS 2018 Graphical Model Inference: Sequential Monte Carlo Meets Deterministic Approximations Fredrik Lindsten, Jouni Helske, Matti Vihola
ICML 2016 Interacting Particle Markov Chain Monte Carlo Tom Rainforth, Christian Naesseth, Fredrik Lindsten, Brooks Paige, Jan-Willem Vandemeent, Arnaud Doucet, Frank Wood
ICML 2015 Nested Sequential Monte Carlo Methods Christian Naesseth, Fredrik Lindsten, Thomas Schon
AISTATS 2015 Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering Simon Lacoste-Julien, Fredrik Lindsten, Francis R. Bach
JMLR 2014 Particle Gibbs with Ancestor Sampling Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
NeurIPS 2014 Sequential Monte Carlo for Graphical Models Christian Andersson Naesseth, Fredrik Lindsten, Thomas B Schön
FnTML 2013 Backward Simulation Methods for Monte Carlo Statistical Inference Fredrik Lindsten, Thomas B. Schön
NeurIPS 2013 Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC Roger Frigola, Fredrik Lindsten, Thomas B Schön, Carl Edward Rasmussen
NeurIPS 2012 Ancestor Sampling for Particle Gibbs Fredrik Lindsten, Thomas Schön, Michael I. Jordan