← Authors

Fredrik Lindsten

31 papers · 2012–2025 · 7 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (13) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (15) πŸ—ΊοΈ Taxonomy Completionist (19) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (7) 🧭 Keyword Pioneer 🌈 Renaissance Researcher (6) πŸ† Keyword Champion (8) 🧬 Topic Evolution πŸ’Ž Century Club (31) πŸ—ƒοΈ Keyword Collector (57) ⚑ Prolific Year (5) πŸ”₯ Unstoppable (8) πŸ“ˆ Trend Setter

Conferences

NIPS (9) AISTATS (7) ICML (6) ICLR (4) JMLR (2) UAI (2) IJCAI (1)

Top co-authors

Joel Oskarsson (4) Per SidΓ©n (4) David Widmann (4) Christian Naesseth (3) Thomas SchΓΆn (3) Filip EkstrΓΆm Kelvinius (3) Amirhossein Ahmadian (2) Tomas Landelius (2) Jacob Roll (2) Michael I. Jordan (2)

Keywords

bayesian inference (11) markov chain monte carlo (9) sequential monte carlo (8) particle markov chain monte carlo (5) uncertainty quantification (5) state-space model (4) variational inference (4) model calibration (3) particle filter (3) graph neural network (3) probabilistic graphical model (2) anomaly detection (2) graphical model (2) particle method (2) probabilistic inference (2) ancestor sampling (2) gaussian markov random field (2) posterior distribution (2) particle filtering (2) hamiltonian monte carlo (2)

Papers

Continuous Ensemble Weather Forecasting with Diffusion models ICLR 2025
Discriminative ordering through ensemble consensus UAI 2025
Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled Diffusion Sequential Monte Carlo ICML 2025
WyckoffDiff – A Generative Diffusion Model for Crystal Symmetry ICML 2025
cryoSPHERE: Single-Particle HEterogeneous REconstruction from cryo EM ICLR 2025
Discriminator Guidance for Autoregressive Diffusion Models AISTATS 2024
Probabilistic Weather Forecasting with Hierarchical Graph Neural Networks NIPS 2024
Unsupervised Novelty Detection in Pretrained Representation Space with Locally Adapted Likelihood Ratio AISTATS 2024
On the connection between Noise-Contrastive Estimation and Contrastive Divergence AISTATS 2024
Temporal Graph Neural Networks for Irregular Data AISTATS 2023
DINO as a von Mises-Fisher mixture model ICLR 2023
Fast and scalable score-based kernel calibration tests UAI 2023
Scalable Deep Gaussian Markov Random Fields for General Graphs ICML 2022
Robustness and Reliability When Training With Noisy Labels AISTATS 2022
Likelihood-free Out-of-Distribution Detection with Invertible Generative Models IJCAI 2021
Pseudo-Marginal Hamiltonian Monte Carlo JMLR 2021
Calibration tests beyond classification ICLR 2021
Markovian Score Climbing: Variational Inference with KL(p||q) NIPS 2020
Deep Gaussian Markov Random Fields ICML 2020
Pseudo-Extended Markov chain Monte Carlo NIPS 2019
Parameter elimination in particle Gibbs sampling NIPS 2019
Calibration tests in multi-class classification: A unifying framework NIPS 2019
Evaluating model calibration in classification AISTATS 2019
Graphical model inference: Sequential Monte Carlo meets deterministic approximations NIPS 2018
Interacting Particle Markov Chain Monte Carlo ICML 2016
Nested Sequential Monte Carlo Methods ICML 2015
Sequential Kernel Herding: Frank-Wolfe Optimization for Particle Filtering AISTATS 2015
Particle Gibbs with Ancestor Sampling JMLR 2014
Sequential Monte Carlo for Graphical Models NIPS 2014
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC NIPS 2013
Ancestor Sampling for Particle Gibbs NIPS 2012