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Fredrik Lindsten

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

Achievements

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+10 more ↓ ๐Ÿงญ Keyword Pioneer ๐ŸŒ‰ Interdisciplinary Bridge ๐ŸŒˆ Renaissance Researcher (6) ๐Ÿ—บ๏ธ Taxonomy Completionist (19) ๐ŸŒ Conference Polyglot (7)
๐ŸŒ Conference Polyglot (7) ๐Ÿงญ Keyword Pioneer ๐ŸŒˆ Renaissance Researcher (6) ๐Ÿ† Keyword Champion (8) ๐Ÿงฌ Topic Evolution ๐Ÿ’Ž Century Club (31) ๐Ÿ—ƒ๏ธ Keyword Collector (57) โšก Prolific Year (5) ๐Ÿ”ฅ Unstoppable (8) ๐Ÿ“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 Sidรฉn (4) David Widmann (4) Christian Naesseth (3) Thomas Schรถn (3) Filip Ekstrรถm 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 โ€“ 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